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CONFERENCE OBJECTIVEThe Conference OBJECTIVE is to intensify the information exchange of the results intheoretical research and practical developments in the field of the e-Learning and mLearning.e-Learning09 is organized under the patronage of the EUROPEAN COMMISSION Directorate-General for Education and Culture, Lifelong Learning Programme.
ORGANIZING COMMITTEE CO-CHAIRS:Robert Fisher (Germany)Stoyanka Smrikarova (Bulgaria) SECRETARY:Kristin Lauterbach (Germany) MEMBERS:Aneliya IvanovaDimitar YorgovLuben IlievNedyalko Nedelchev
TABLE OF CONTENTSPLENARY SESSIONPS.1 The New Generations of Students and the Future of eLearning in Higher EducationAneliya Ivanova, Angel Smrikarov .. 17PS.2 Enterprise Information RetrievalLeon Rothkrantz ...... 26
SECTION 1e-LEARNING INNOVATIONS AND TRENDS
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SECTION 2e-LEARNING SOLUTIONS
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e-Learning09THE DIGITAL NATURE OF OUR FUTURE STUDENTSWhen we consider the influence of technology on Generations X, Y and Z, wecould summarize the following (Table 1):Table 1. Technology influencesDigital Revolution:Gen X faced the Digital revolution.Gen Y came at age during the DR.Gen Z were born in a Digital WorldDigital Technologies:Gen X started using Digital Technologies.Gen Y embraced the DT.Gen Z can not live without DTInternet:Gen X and Y went on-line.Gen Z were born on-lineWhich technologies shaped the generation?As children Gen X were shaped mostly by TV, Video and Video Games.Gen Y were shaped by PC, Computer Games and WEB.Gen Z have at disposal all the DT and gadgets availableFor Gen Y, the continuous interaction with Digital Technologies has developed:
We just have to emphasize these features in order to get a notion about the natureof Generation Z The Digital Kids.When we are seeking to find the most relevant way to teach Gen Y, it is veryimportant to have an idea about the way our present and future students perceive thetechnologies. We usually say: I am using technologies in my courses, but do ourstudents really take them for technologies? The true is: they do not. They dont think interms of technologies, they think in terms of interactions. Computers, mobile phonesand WEB are not a technology. They are like roads just have been there. They are notinterested how a technology works they just want to use it. They are not likely to try toprogram a computer. It is just a tool to enter their virtual world. And this is much more ofimportance for the Generation Z.THE SOCIAL NATURE OF OUR FUTURE STUDENTSWhen we consider the influence that parents have exerted on Generations Y andZ, we could generalize: as children Generation X were loosely controlled by parents they were so called latch-key kids. As a result they became self-directed andindividualists. Generation Y were nurtured by helicopter parents all the time decidingfor their kids, so they became rule-followers, close to their parents and teamoriented. Generation Z are grown up mostly by Generation X parents, so they are likelyto become more self-directed and less team-oriented than Generation Y.- 18 -
e-Learning09What should we know about Generation Y students? Their parents treated them as special, decided for them and organized theirlives. They have grown up with a huge array of choices and believe this is theirbirthright. They are very pragmatic and rationalistic with strong career orientation. They dislike doing any work that is not relevant to their personal goals. They are confident and tend to overestimate themselves. They dont respect authority, expertise, science and traditional sources ofknowledge. Previously they have been target-based taught, tested, guided and mentored,so they have lost important for University study skills. Their learning style is visual-kinesthetic. Having short attention spans, they are poor finishers, due to their lack ofpatience and persistence.Why Generation Y are bored and detached by the traditional training at theUniversity? the delivery goes in a slow pace, with linear and logically sequentrepresentation of knowledge; there is no immediate reward of efforts; the assignments are individual and require critical thinking, reflection andcreativity; the training process is tied to the curricula low flexibility and selectivity ofcourses.Analyzing the nature of Generation Y we can point out the followingCRUCIAL CHALLENGES TO THE HIGHER EDUCATION24/7 FacilitatorYears on end the students have been respected by the authority of the lecturersand have adapted to their teaching style. Now it is clear that the teacher-centered modeldoes not work anymore. Like it or not, our students cannot afford to engage in lots ofeducational exploration, improvisation or open-ended spontaneity. The heyday of thebrilliant, if diffuse, lecturer whose wisdom might just change lives is over. [1]Nurtured by Helicopter parents who decided for them and organized their livesfrom birth now, the incoming students will be absolutely helpless at the University life.So, they will insist on continuous and immediate help and guidance instead of trying tosolve their problems and do their assignments alone. Generation Y students are morelikely to treat their lecturers as facilitators rather as instructors. They would hardlyappreciate and be inspired by lecturers knowledge, expertise and experience.Contrariwise, some authors describe this generation as skeptic (not respected byauthority, experts and traditional sources of knowledge) [2]. But at the same time theywill need lecturers mentoring to achieve their personal goals getting high grades andcredits.Personalized and Adaptive TrainingSince their early childhood Generation Y are addicted to environments (games,services, technologies, and all the gadgets they use) adapting to their preferences.When they come to the University they will expect the same adaptivity. Grown up asspecial, all the time having a variety of choices, the future students will insist onflexibility, personalized training and services, service selectivity.- 19 -
e-Learning09Negotiation for GradesSince they show general distaste for doing busy work that is not relevant topersonal goals, respond best to external motivators and are highly rationalistic [3],they will overestimate their efforts and will negotiate for grades.Lack of Qualities for University StudyFrom the Nursery they have been target-based taught, tested, guided, andmentored, so they have lost very important qualities as self direction, critical andinnovative thinking, reflection of learning, creativity, exploration skills, understanding ofabstract scientific concepts that are absolutely necessary for successful study at theUniversity.Researchers at King's College London asked 800 young teenagers to take testsmeasuring their understanding of abstract scientific concepts. The results showed thatthe number of exceptionally intelligent teenagers is far lower than a generation ago [4].Professor Michael Shayar, who led the study, believes the decline in brainpowerwas due to target-based teaching. "The moment you introduce targets, people will findthe most economical strategies to achieve them. In the case of education, I'm sure thishas had an effect on driving schools away from developing higher levels ofunderstanding."University IT environmentFor each successive generation technology is only technology if it was inventedafter they were born. [5]A challenge for Higher Education is the increasing gap between the institutional ITenvironment and the technology environments that Generation Y have created forthemselves [5]. Todays students are avid users of Web, e-mail, telephones, and otherIT resources; however, their rapid adoption of instant messaging, cell phones, blogs,wikis, social networking Web sites, and other resources that are not generally part of thecore campus infrastructure leads to a host of new concerns.After specifying the challenges we can outline the most importantMISSIONS OF HIGHER EDUCATION1. The first mission of Higher Education is to provide adequate training tostudents, whose behavior, preferences, expectations, thinking and learning styles arefar different from previous students ones and to prepare itself for the incominggeneration.2. Despite of not having qualities for University study, Generation Y is stillstruggling to go to the University. However, this choice is not prompted by their wish forself-development.Generation Y were born when the economic conditions were growing rapidly. Aschildren they were financially secure and lived with comforts. These kids who were bornduring the economy boom do not know how to adjust with less. However, at the sametime they have seen their parents work hard and hence know the value of hard work.All these circumstances have formed a strong career orientation as theimportance is set to payment. According to a research [6], pay is clearly the mostimportant benefit for the future and cash is the best performance incentive forGeneration Y.The Generation Y is the largest in population living generation and the firstrepresentatives have already become employees. How do they perform at theworkforce market?Sue Honore, learning consultant at Ashridge business school has started a majorresearch project to uncover how employers will need to adapt training and developmentto suit the Generation Y [7]. "They expect employers to help them grow. Coaching and- 20 -
e-Learning09mentoring is a big part of this and they seem to respond positively to it," "I think we willprobably start to move away from the idea of fixed formal training and move moretowards flexible coaching and mentoring systems with Gen Y." Honore shares.Here is the second mission of Higher Education to develop the missingprofessional skills before the graduates enter the workforce market and to teachGeneration Y how to become successful employees.3. The notion knowledge society emerged toward the end of the 90s and isparticularly used as an alternative by some in academic circles to the informationsociety [8]. According to a definition the Knowledge society "creates, shares and usesknowledge for the prosperity and well-being of its people". Who actually creates thistype of knowledge? Such type of knowledge is created by scientists as a result fromsustained research work. Nowadays it is created by Baby Boomers and Generation X,but who will create it in the future?We can be pretty sure the term knowledge society was coined by Baby Boomers.If we ask the Generation Y and Z to label the society they live, they would probablystamp it as fun society. Inspired by commercials, TV shows, and all the entertainingstuff offered by WEB and mobile technologies, they actually live for fun. To be sure,they are knowledge creators but the knowledge they consider as valuable is focused tocelebrities, life-style, luxury, entertainment, fashion.The other type of knowledge that we consider as valuable, they just consume inorder to get diplomas and well paid jobs. They are able to acquire only short-termknowledge and do not manage to reach reflection because their brains are constantlyoverloaded by the digital lives they live.The research uses logical and systematic procedures in the comprehension andtransformation of the reality, in order to elaborate new knowledge. That activity requiresfrom the researcher, curiosity, scientific knowledge, creativity and persistence. [9]Unfortunately, our students do not have these qualities. As mentioned before,Generation Y is missing out a large segment of learning that of exploring more deeply,enquiring, and reflection. For this generation to be successful as the thinkers andleaders of the future they have to acquire those skills.In 1995, within an experiment a group of students were asked to think about listingthe traits of a good scientist. The children's list included that a scientist must have theability to read and write, be a good listener, pay attention to what they are looking at,have lots of ideas, study unknown things, and have patience. A visiting physicsprofessor from UC Berkeley reviewed the list and stressed to the children that the keywhich they had forgotten was that a good scientist needs to be skeptical, ask questions,and not accept everything. A good scientist needs to continually ask why. A mission ofour educational system must be to revitalize our children's imaginations. It is critical thatwe teach our children how to think abstractly and in a nonlinear fashion. [10]The author of [10] discussed the WEB as one of the means that will solve theproblem, but unfortunately, 14 years later we see that although WEB helped indevelopment of non-linear thinking, it actually contributed to killing the imagination andabstract thinking.We believe the third mission of Higher Education is: to revitalize the Gen Y's interest to science; to teach Gen Y how to become researchers; to develop the missing learning segment critical thinking, exploratorylearning, reflection, creativity and persistence.
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e-Learning09WHY SHOULD WE BOTHER ABOUT GENERATION Y AND Z?The answer is pretty simple: Just because we are their creators. Baby Boomersand Generation X gave them birth and nurtured them, and Generation X developed thetechnologies that shaped them this way. As Jeff Gordinier, author of the book X Savesthe World, shares: "We've created all these great Websites that now Millennials wastetheir lives on."THE FUTURE OF e-LEARNING IN HIGHER EDUCATIONBefore presenting our vision about the future of e-Learning in Higher Education,lets consider how the learning occurs for Generation Y.We can honestly generalize that at present, the learning process for Generation Yis separated in two parts: formal part at the University, where they go by necessity, without internalmotivation, only to get credentials and as a result they are constantly boredwith their formal learning; informal part outside the University, where they are fully engaged, exitedand motivated in their personal virtual spaces, including social networks,social media, social bookmarks, shared resources, IM communication.When the formal part is weakening, the professional skills and knowledge ofour graduates (actually the quality of our product) are becoming worst than ever.What is actually happen outside the University? It is just WEB 2.0 a social rathera technological phenomenon the focus is to the people, connections and resourcesharing.The social WEB 2.0 services should not be underestimated except for fun, theyare largely used for a very creative purpose as powerful tools for exchange ofknowledge, ideas and experience between professionals and researchers. There aremany examples of blogs that became research and professional forums, even virtualplaces for workshops and conferences. Social networking usage is only going toincrease. According to Netpop Research, there has been a 93% increase in socialnetworking usage since 2006 and a study by Pew reveals that 75% of the 18- to 24year-old age group have a profile within a social network [11].Its clear that we have to engage our students back to the formal education andthe way we could achieve this is through integration of students favorite and daily usedsocial-WEB tools in the training process.Many lecturers have adopted some of the digital nature of Generation Y, usingintensively IT in their research work and private life. But it doesn't mean they are readyto educate this generation, not to mention the Generation Z. In order to understandthese students, we have to understand their favorite WEB tools and services, but not interms of technology, rather as behavior connectivity, engagement, interaction.The existing Learning Management Systems (LMS) could not anymore catch andhold the attention of Generation Y, since they place the student in a passive role of fixedcontent recipient. Actually the Y-students expect to be connected, community engaged,actively interacting and collaborating in a personalized learning environment,constructed and managed by them.In 2005 a group of educational technologists starts the discussions aboutpersonal learning environment (PLE), and soon Scott Wilson from CETIS presents aconceptual model of next generation virtual learning environment. This model is the firstgraphical representation of PLE. Derek Wenmoth further develops the model,representing the interaction between PLE and existing LMS. The majority of LMS,currently in use, are server-based, and designed to be used as institutional systems. Inthe light of the idea about Virtual University, this creates a problem for students movingbetween institutions, since they may need to learn how to use different environments- 22 -
e-Learning09based at different institutions. The most significant benefit of a PLE is that the studentshave one set of tools suited to their needs, which they can use to interact with a numberof different institutions throughout their studies.Actually we believe the evolution of e-Learning in Higher Education shouldtake the following direction: the existing e-Learning environments participating infuture e-Learning as content repositories, but the active part of learning processcovered by PLE, where the students, using their favorite WEB services and tools,will construct their knowledge in constant collaboration.The challenge of evolving pedagogy to meet the needs of Net-savvy students isdaunting, but educators are assisted by the fact that Generation Y values education. Butwhat about meeting the needs of Generation Z?WHAT TO EXPECT FROM GENERATION Z?Being born in a digital world, the Generation Z adapts to technology like noother generation and highly depends on it.They are very individualistic, and may not give too much importance to familyvalues. They also will be not team players.They are less career-oriented than the Generation Y but have strongopinions and do not take suggestions well. Psychologists are noticing a drasticbehavior change from Generation Y to Generation Z. For the Generation Y, theemphasis has always been career and education whereas the Generation Z does notbelieve in career and formal studies either. Education and work plays a minimal rolein their lives and they do not see them as means of survival. By the time theGeneration Z starts working there might be severe shortage of professionals likedoctors and scientists. But such professions may not hold any value for Generation Z.Their communication takes place mostly on the WEB and they take it forgranted. Therefore they show very little verbal communication skills and may not dovery well in areas of public speaking. They also lack expression, confidence andinterpersonal skills. In these qualities they are absolute opposite to Generation Y.Generation Z processes information at lightning speed. They are used toinstant action and satisfaction due to Internet technology and may be very impatient asthey desire instant results.Generation Z lives in the virtual world and they can reach out to any place throughthe WEB. They are less likely to travel and step out of their homes for anything. Theywill try to get everything to the place they live.Generation Z being bad listeners have less regard for what others have to say andthey do not pay attention to others. They dont believe in living according to socialnorms. Their society exists on the WEB where they speak their mind out and expresstheir opinions.Resli Buchel reveals ten reasons why the Internet-loving kids of today will becomethe technology savvy adults of tomorrow [12], facetiously proposing The TenCommandments of Generation Z:1. You shall always be connected2. You shall always be mobile3. You shall use computers at work4. You shall use computers at play5. You shall have many friends that you will never meet6. You shall have a second life7. You shall be ultra-independent8. You shall multi-task9. You shall always be in demand10. You shall invent a whole new language- 23 -
e-Learning09CONCLUSIONS AND FUTURE WORKIt is more than clear that the Higher Education could not stay untouched by theradical changes in the nature of present and incoming students.In order to survive as a vital institution the University should undertake crucialtransformations concerning the teaching approach, the way of communication with thestudents and the organization of administrative services.When we consider the training process, the adoption of WEB 2.0 services and eLearning 2.0 techniques is unavoidable if we aim at catching up Generation Y and Zstudents. It is also necessary to directly engage the students in a dialogue about howthey would like to see the facultys use of technology to help them learn moreeffectively.At the same time it is important for the academic staff to be aware thatcustomization is central to the definition of technology for Generation Y and Z students it is something that adapts to their needs, not something that requires them to change.The incoming students will have significant expectations regarding the use oftechnology to support learning, but, those expectations are tied to faculty members andtheir ability to use technology correctly.According to administrative issues, Generation Y and Z will expect their problemsto be solved quickly and easily and the use of technology to improve student serviceswill be critical to the University, since the students will expect services delivered throughtechnology.The Generation Z will place more daunting challenges to the Higher Education andthe sooner we start preparing our training approach to address their learning style, thebetter we will manage to attract them to education, research and science.REFERENCES[1] Taylor,M.L. Generation NeXt comes to college. 2006 updates and emergingissues, A Collection of Papers on Self-Study and Institutional Improvement, Vol. 2,2006.Available electronically at:http://www.taylorprograms.org/images/Gen_NeXt_article_HLC_06.pdf[2] Wilson, M., L. E. Gerber. How Generational Theory Can Improve Teaching:Strategies for Working with the Millennials, Currents in Teaching and Learning, Vol.1,No. 1, Fall 2008.[3] Sweeney, R. Millennial behaviors and demographics, 2006. Retrieved 21September, 2008 from: http://library2.njit.edu/staff-folders/sweeney/Millennials/ArticleMillennial- Behaviors.doc[4] Logan, G. Generation Y prove teenage intelligence is in decline, October 2008.Retrieved from: http://www.personneltoday.com/articles/2008/10/27/48120/generationy-prove-teenage-intelligence-is-in-decline.html[5] Hartman, J., P. Moskal, Ch. Dziuban. Preparing the Academy of Today for theLearner of Tomorrow, in Educating the Net Generation, EDUCAUSE, 2005.Available electronically at: www.educause.edu/educatingthenetgen/[6] Personnel Today: Generation Y Research by Ipsos MORI, August, 2008.Available electronically at:http://www.personneltoday.com/Assets/GetAsset.aspx?ItemID=6798[7] Research at Ashridge college: Generation Y Research. Available electronicallyat:http://www.ashridge.org.uk/Website/Content.nsf/wFAR/Generation+Y+Research?opendocument[8] Burch, S. The Information Society / the Knowledge Society, in Word Matters,May, 2006. Available electronically at: http://vecam.org/article517.html- 24 -
e-Learning09[9] Lemos de Menezes, N., F. A. Villela. Scientific Research, SEED NewsMagazine, Main subject of March / April - year VIII n. 2[10] Rose, K. Learning with the World Wide Web: Connectivity alone will not SaveEducation. Proceedings of the INET'95 International Conference, Hawaii. Reston, 1995,VA: Internet Society, 273-278.[11] Why does engaging Gen Y on the social web make sense? Availableelectronically at: http://www.currencymarketing.ca/blog/Why-does-engaging-Gen-Y-onthe-social-web-make-sens[12] The Ten Commandments of Generation Z, Time Out Sydney, Availableelectronically at: http://www.timeoutsydney.com.au/kids/the-ten-commandments-ofgeneration-z.aspxABOUT THE AUTHORSAneliya Ivanova, Principal Lecturer, PhD, Department of Computing, University ofRousse, Phone: +359 82 888 827, -mail: firstname.lastname@example.com Smrikarov, Asoc. Professor, PhD, Department of Computing, University ofRousse, Phone: +359 82 888 249, -mail: firstname.lastname@example.com work was partially supported by the European Social Fund and the BulgarianMinistry of Education and Science under the Operation programme Human ResourcesDevelopment, Grant # BG051PO001/07/3.3-02/7.
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INTRODUCTIONInformation Retrieval can carried out in several environments, two of these are:Web and Enterprise. For each of these environments a different type of Search Engineis required namely Web Search Engine or Enterprise Search Engine. Web Searchentails, using a search engine to identify and retrieve information housed on the WorldWide Web. While an Enterprise Search focuses on retrieval of information withinEnterprises (i.e. Companys), this research focuses on the Enterprise Searchenvironment. A term within Enterprise Search that is getting a lot of attention isFindability. Its definition according to Frappaolo & Keldsen 2008 [4] is The Art andScience of making Content Findable. The reason for this attention is that the volume ofcontent within businesses is growing at a phenomenal pace, and finding relevantcontent using simple queries is not sufficient, so the need arose for Enterprise search tobecome a science hence the term Findability. They also state that the ineffectivenessof Enterprise Findability is not the fault of a poor search engine but the design behind itsdeployment is flawed. These are some current issues within Enterprise Search, butwhen looking at this domain from a different angle it is possible to identify severalontologys namely the user, the system and the network. Each of these has its own setof hurdles, within the first ontology the user; there are cognitive aspects like, how a userdefines a query, how does a user assess the relevance of a document etc. Furthermorethe user interface plays an important role in the last aspect mentioned.The system has its own hurdles, such as how to convert a users query intoapplication language so the system understands what to retrieve based on the usersquery. Another issue is how to represent and store the data for the search engine toaccess this quickly, keeping the response time to a minimal. The last ontology toconsider is the network, one of the reasons is that network lag can seriously affect theresponse time of the Search Engine. It is also important to consider on what kind ofsystem the search engine runs, because when running it from a PDA the network- 26 -
e-Learning09response time is slower than when using a computer. The topic of this research is todesign, implement and evaluate a system for Enterprise Information Retrieval, whichemploys an improved search result presentation technique. This research focuses onthe user and system aspect of Enterprise Search.There are three challenges that lie at the centre of this research: The first one isthat formulating and re-formulating good search queries is proved to be a cognitivelychallenging task for users. These search queries are often approximations of a usersunderlying need, making it difficult to formulate a query in one try, to satisfy this need;consequently making the information seeking process an iterative one [5, 10, 11]. Thesecond one is interpreting and assessing the relevance of documents in search resultlist, this is also imperative to the search process [5, 12]. Users are reluctant to examinelarge numbers of individual documents and they hardly look further than the first resultpage [5, 13]. So a users decision to view a document or not is totally dependent on theavailable elements within the search results namely: document name, author, file type,snippets, etc. Lastly building a system that allows a user to search trough a dataset,which assists with re-formulating search queries and interpreting and assessing therelevance of documents retrieved. The first two challenges are user related while thelast is associated with the system ontology.RELATED WORKBy exploring current Enterprise Search systems, information could be gathered onhow to tackle the last research challenge mentioned above. The anatomy of Googles1architecture was reviewed to get a better understanding of the components of largescale search engines. Google was chosen because their success and ingenuity, whichcould be used in the development of the new Enterprise Search system. Also thedifferences between the components of an Enterprise and Web search engine areminimal. These components are shown in Figure 1 and discussed in the followingparagraph.
e-Learning09Enterprise Search system, because of the positive results of several studies using oneof these or both of these components (Joho and Jose 2006[5], White et al 2005 [6],Tombros and Sanderson 1998 [15] and Dziadosz and Chandrasekar 2002 [7]).Finally Solr 1.2 Search server2 and IBM Omnifind Search Solution3 were exploredfor their Indexer and Searcher components. Seeing that the main focus of this researchwas not to implement these components, and since an implementation already existsand which is well tested, the development of the Indexer and Searcher seemedunnecessary. Solr and IBM Omnifind were researched to be used for these componentsof the new Enterprise Search system. The Solr Search server was chosen because itsan open source project and could be manipulated to provide all the functionality neededfor the prototype; while the IBM Omnifind Solution was less adjustable to the Basoraprototype development requirements. It was also very difficult to access the Indexer andSearcher components of the IBM Omnifind Solution for integration into the Basoraprototype. Another key factor was that the Solr server, was well documented and tested;the documentation was easily accessible, unlike the documentation for the IBMOmnifind Solution. This was very hard to obtain, often it was incomplete or tosuperficial. It was possible to contact the IBM support line, but this was usually quite alengthy process. In the end Solr 1.2 was chosen because of its speed, agility and gooddocumentation.GLOBAL DESIGNThe design of the BES prototype can be split up into the components seen inFigure 2. Each of these components performs a vital role in order to allow the prototypeto function. The Crawling, Parsing and Committing components are also referred to asthe Basora Pre-processing stage, because this has to be done before-hand for theprototype to function.
e-Learning09sending the Parsed XML files to the Indexer. It reads the parsed XML files one by oneand sends them to the Indexer. Which has to index these by storing and caching them;for when a search query is submitted to the Searcher it can quickly retrieve the relevantdocuments. The Indexer and Searcher components of the BES prototype have beenconfigured using the Solr Search server. These components were used becausedeveloping these is outside of the scope of this research and that it is already availableand that Solr is a well respected and tested Search Server. The results are returned bythe Solr Searcher in XML format, this contains all the necessary data to be able toextract the TRS. This is part of the responsibility of the Basora Engine; its main purposeis to function as a bridge between the Solr Searcher and the Basora Web Interface. Asubmitted query first goes through the Basora Engine where it is formatted and sent tothe Solr Searcher. Then the results it obtains are parsed and formatted so these can bedisplayed to the user through the Web Interface. This interface offers an improvementover existing Enterprise Search systems. It addresses the first two research challenges(discussed in the first chapter) by adding a visual and a textual document summary tothe result presentation namely Top Ranking Sentences (TRS) and a Thumbnail. TRSare up to three sentences presented in the search results that contain the query termsand are ranked according to several features. Their main advantage is offering the userextra information about a document so beforehand they can form a better opinion of adocuments contents [6]. The thumbnail is a miniature image of the first page of adocument, this increases ability of a user to make more accurate decisions about therelevance of results [7]. These elements are further discussed in the next chapter.IMPLEMENTATIONThe Basora Enterprise Search (BES) prototypes main purpose is to make adataset searchable while assisting a user with query reformulation and relevanceassessment. The design consisted of various components as discussed in the previouschapter. These were implemented using an agile software development approach,incrementally developing BES prototype. The Crawling Parsing and Committercomponents are the pre-processing steps of the BES system. These were developedfrom scratch using some essential libraries namely Apache POI & PDFBox (for readingPDF and Office documents). The pre-processing components are responsible forretrieving all documents from a file system, extracting the metadata and content fromthese, formatting this into indexable XML and finally committing this to the Indexer.The Apache Solr Server was customized to provide Indexer and Searcher support forthe BES system; Solr is a subproject of the Apache Lucene open source Java searchlibrary. It ensures that the pre-processed data is properly stored and also once a queryis submitted to the Searcher it retrieves the results as quickly as possible. The BasoraEngines main purpose is to allow the Web Interface and the Solr Searcher tocommunicate. It is also responsible for extracting the TRS, and formatting the results inHTML so they can be displayed to the user through the Web Interface. This userinterface allows users to interact with the BES system during the extent of their search.A screenshot of the main interface is shown in Figure 3.
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e-Learning09Equation 1. Total Ranking Calculation.
e-Learning09PERFORMANCE EVALUATION AND RESULTSThe performance and user evaluation was carried out on the same computer andusing the same fixed dataset. The computers specifications were: Intel Core 2 DuoT7500 2.20 GHz (2 CPUs), 800 MHz FSB, 2 GB of RAM, 100 GB Hard disk drive, andruns Windows XP Professional Edition SP2. The dataset consisted of 5408 documents:50% Microsoft Word, 5% PowerPoint, 8% Excel and 37% PDF documents. For theperformance evaluation the following variables were measured: the time it takes to preprocess the dataset, response time of the search engine and the average number ofresults it returns. These were taken from an Enterprise Search Engine evaluation guideby Google [14]. This document describes seven critical characteristics of an EnterpriseSearch Engine. Only three aspects were evaluated, because these were needed toindicate how the Basora Enterprise Search (BES) prototype performs compared tocurrent Enterprise Search Solutions. Some of the other characteristics mentioned in theevaluation guide were: Security, How often it crawls the content, Costs andmaintenance fees, these were outside of the scope of this evaluation. These variableswere measured for both the BES system using layout 4 and the IBM Omnifind 8.5Enterprise Search solution (used as benchmark).Table 1. Pre-processing performance results
First both systems were installed on the test computer, then both systems were puttrough their pre-processing steps and clocked. In Table 1 the pre-processing times areshown, these indicate that the BES and Omnifind systems pre-processing time is moreor less equal. There is a big difference between both systems: Crawling and Indexingtimes, because the exact procedure used within the Omnifind solution is not availableone could only speculate why. One of the reasons could be that BES does not containall the security and back-up features that the commercial solutions have. Another validreason could be that the BES solutions parsing utility directly generates indexable XMLthat can be posted to the indexer. This procedure is probably different within thecommercial Enterprise Search solution.Table 2 shows the average response time and average number of results obtainedfrom the search engine. This was carried out on both Enterprise Search systems, byfirst gathering 30 search queries from experts that continuously query the availabledataset for information; then executing these on both the systems and gathering thedata. BESs average response time was only 75 milliseconds longer than IBM Omnifind,while providing the user with two extra forms of document summaries. Collins 1994 [6]states that a user considers a process to be real time, if it takes less than 100milliseconds; so this difference is not noticeable by the users, so they will not getfrustrated by the time it takes for the results to be returned. Additionally the BES systemreturns about 11 times more search results then the IBM Omnifind system; this could bebecause the Omnifind engine has a stricter filtering function, but it could also indicatethat the Omnifind engine indexes less of the documents contents than the Basoraprototype does.- 32 -
e-Learning09Table 2. Average Response Time and Average Number of results returned
e-Learning09values were obtained during the evaluation procedure using a custom BES evaluationadd-on.Table 3. User Interaction
The query length column indicates that longer queries were submitted when usingthe fourth layout so the users formulated more specific queries when using this layout,this could suggest that they used the extra information available to reformulate morespecific queries. The most documents were clicked when using the first layout; thisindicates that the information provided in the baseline was not sufficient so more oftenthey had to open a document to find out if this was relevant for their search. This wasalso seen during the user evaluation that users clicked fewer documents when using thelayouts 2-4. Another interesting factor is that seeing that the underlying search engine isidentical for all layouts and the users clicked fewer documents per result page whenusing layouts 2-4. It can be suggested that the participants interacted with the systemmore often when using the document summary layouts then when using the baselinelayout. So TRS and thumbnails appear to increase the interaction between the user andsearch engine. From these results nothing concrete can be concluded because thestandard deviations are all quite large, meaning that the results are spread broadly overthe result space. Also the differences between the average values between the layoutsare not very big, these only suggest slight differences. This may also be because of theassignments used; it is very difficult to set up assignments, which actually give a goodrepresentation of reality.Relevance AssessmentThis paragraph presents the participants relevance assessment from threeperspectives as shown in the table below from the left to the right column respectively:Ease of finding information using the current layout, Easy of finding new Informationwith the current layout after query reformulation and Easy with which the user couldpredict the contents of the documents based on the information available the layout.For each of the perspectives the participants were asked to fill in a score, between 1and 7, 1 indicating best/easiest and 7 worst/hardest. The values in each of the cells arethe average and the standard deviation (between brackets).
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e-Learning09Table 4. Relevance Assessment
The ease of finding is greater using the third and fourth layout; this suggests thatthe thumbnail contributed the most in facilitating the ease of finding for the user. Whilefor the baseline and second layout the value is the same, this indicates that the ease offinding with the second layout is equal to the baseline, so the TRS do not contribute tothis measure. The layouts 2-4 are preferred over the baseline layout when reformulatinga query to attain new information. There is a definite difference between the baselineand the document summary layouts, which indicates that these assist a user with queryreformulation. For contents prediction the results show that layout 4 is best followed bylayout 3 & 2 and finally layout 1. This indicates that the contents prediction increases asthe elements TRS and Thumbnails are added to the interface, this suggests that theusers prediction accuracy increases as more information is available, which is quite alogical occurrence. This was also noted during the user evaluation that the users couldcomplete several assignments faster, because the extra information allowed them toform better opinions on what a document contained. For each of the perspectives thebest rated overall is it the fourth layout, this suggests that this is the most optimal layoutand maximizes the users relevance assessment.Contribution of Layout ElementsIn this paragraph the results obtained from the user tests concerning thecontribution of the layout elements are presented. Each of the layout elements got ascore from the participants that corroborated how much each of them contributed to theinitial relevance assessment. The scores assigned were between 1 and 7, 1 indicatingvery much and 7 very little. The following layout elements were evaluated: Filename,Author, Snippet, TRS, Thumbnail, File type and File size. The results are shown inTable 5 where a strong contribution is represented by a low score. The values in eachof the cells are the average and the standard deviation (between brackets). Note thatthe sample sizes differ across the layout features seeing that the layout elements TRSand Thumbnail were not available in all the layouts.Table 5. Contribution of layout elements
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e-Learning09From the total row can be concluded that the participants found the filename wasthe strongest factor when determining whether or not to open a document. While thethumbnail comes second, again demonstrating its importance as in the previousparagraph. The difference between the TRS and the Snippet is not very significant but itdoes indicate that users tend to use the Snippet more. This could be because theparticipants are accustomed to the Snippet being a crucial element within the resultpresentation, and that they still have to get use to the TRS. This was also mentioned byseveral participants after the evaluation.When the TRS and Thumbnail results are compared it is clear that TRS is given astronger score for task 2 a decision making task, while the thumbnail is given a strongerscore in task 4 a known item task. This suggests that the effectiveness of TRS andThumbnails varies across tasks. And this also indicates that TRS may be more usefulwhere Thumbnails are less effective, and vice versa. Another interesting observation isthat the Thumbnail is given a very high score for the tasks 3 and 4, while the TRS isgiven its strongest score for task 2. This can be explained because for task 3 and 4 theuser had to find documents of which the front page was known, so the thumbnail madefinding the documents a breeze. While for task two the user had to search trough thedataset to figure out whether or not a project had been carried out or not, so for this taskhaving extra information of the TRS was very useful.A very interesting observation is that the author element was not rated very highfor the first task where the objective was to find the name of the author corresponding toa certain project and vice versa. This was mainly because the author field information isextracted from the metadata that is gathered from the document and this is not alwayscompletely accurate. Sometimes it contains nothing or a useless variable. It could besuggested that a better means of acquiring the information stored in the metadatashould be considered, or the use of metadata should be encouraged or facilitated.Layout Preference and Participant CommentsOnce the participants had completed all the tasks they were asked to rank all thelayouts according to their preference. The scores 1 to 4 were used, 1 being the onepreferred most and 4 least. In the figure and table below the results are presented. Theclear winner among the layouts is layout four with an average of 1.5 followed by layout 3as the runner up. Twenty of the thirty-two participants choose layout 4 as the preferredlayout, while twenty-three participants choose layout 1 as the least preferred one. Someof the users did not rank layout 4 as the winner because they think it is a bit toocluttered, and they are used to the current search result presentation. This suggeststhat the new layouts take some getting used to.Table 6. Layout preference
e-Learning09that TRS were experienced as being very handy, but the users did have to get use tothe idea of them. Some users also thought this was a redundant element, othersthought that maybe the TRS should replace the snippet. The thumbnails seemed to bethe participants preferred addition; this indicates that this element can be very usefulwhen performing a particular type of search. One of the experts implied that is was veryobvious how for each of the evaluation tasks an optimal layout exists, showing thatmaybe a user should be able to choose a layout based on the search task that has tobe carried out. The overall positive performance of layout 4 could also be that it offers asupport in a wider range of tasks that layout 2 or 3.CONCLUSIONSThis research demonstrates that this new Enterprise Document Retrieval system,Basora Enterprise Search actually helps a user assess the relevance of a documenteasier minimizing the amount of documents that need to be opened before the userfinds the desired one. It also suggests that the additional visual and textual documentsummaries assist the user when reformulating a search query, also decreasing the timeit will take a user to complete a search task. And finally it also indicates that it ispossible to build a system that performs more or less equal to the to the currentresponse time standards of Enterprise Search Engines, containing these extra usefulcomponents.ImplicationsThe results from this research project have several implications for the design ofEnterprise Search Engine interfaces. Especially because they demonstrate that theaddition of these textual and visual document summary elements has a positive effecton relevance assessment and query reformulation. Also because the system performsmore or less equal to the to the current response time standards of Enterprise SearchEngines, makes is attractive to consider adding this improved search result visualizationtechnique to current Enterprise Search Interfaces. The participants of the userevaluation often found it easier to find relevant documents and new information whenTRS and Thumbnail elements were added to the search result interface so thissuggests that the current Enterprise Search engines result presentation is notnecessarily optimised and that there is room for improvement. The BES system is notcompletely optimized, especially the pre-processing steps parsing component becausethis relies on open source Java libraries that are work in progress. Another minor issueis that support should also be built in for the new Office document format, seeing that itspopularity is increasing. These are some of the aspects that still need optimization buteven so the system demonstrates that it can improve a users search experience. It isnot the intention of this research project to suggest that the current interfaces areuseless, because this is surely not the case seeing that these have been used yearsand they are effective. But the intention is to show that the evolutionary cycle ofEnterprise Search Engine Interface design, has reached a new stage. Seriousconsiderations should be made in favour of this advance, because the addition of thesedocument summaries not only facilitates query reformulation and relevanceassessment. But also increases the level of interaction between the user and the searchengine interface; also assisting in closing a gap the science of Human ComputerInteraction.REFERENCES[1] Benyon, D. and Turner, P. and Turner, S. 2005, Designing InteractiveSystems, 1st edition, Harlow Essex England. 2005.
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e-Learning09[2] Langville, A.N. and Meyer, C.D. 2006, Googles PageRank and Beyond: TheScience of, 1st edition, Princeton University Press, Woodstock Oxfordshire England,2006[3] Collins, D. 1994, Designing Object-Oriented User Interfaces, 1st edition, TheBenjamin/Cummings Publishing Company Inc. Redwood City, California, 1994Papers[4] Frappaolo, C. and Keldsen, D. 2008, Findability: The Art and Science ofMaking Content Easy to Find, in Market IQ Intelligence Quarterly Q2 2008, AIIM, 2008.[5] Joho, H. and Jose, J.M. 2006. A Comparative Study of the Effectiveness ofSearch Result Presentation on the Web, In: Proceedings of the 28th EuropeanConference on Information Retrieval, volume 3936 of Lecture Notes in ComputerScience, London, UK, April 10-12, 2006, pages 302-313, Springer-Verslag, 2006.[6] White, R.W. and Jose, J.M. and Ruthven, I. 2005, Using Top-RankingSentences to Facilitate Effective Information Access, Journal of the American Societyfor Information Science and Technology, 2005.[7] Dziadosz, S. and Chandrasekar, R. 2002, Do Thumbnail Previews Help UsersMake Better Relevance Decisions About Web Search Results?, in proceedings of the25th annual international ACM SIGIR conference on Research and development ininformation retrieval , 2002.[8] Brin, S. and Page, L. 1998. The anatomy of a large-scale hypertextual Websearch engine, in journal of Computer Networks and ISDN Systems, Elsevier, 1998[9] Borlund, P. 2000. Experimental components for the evaluation of interactiveinformation retrieval systems, In: Journal of Documentation, MCB UP Ltd, Volume 56,Issue 1, pages 71-90. 2000.[10] Belkin, N.J. and Oddy, R.N. and Brooks, H.M. 1982, ASK for informationretrieval: Part I. Background and theory. Journal of Documentation, 38(2): pages 61-71,1982.[11] Belew, R. 2000, Finding Out About Search Engine Technology from aCognitive Perspective, Cambridge University Press, 2000.[12] Hearst, M.A. and Pederson, J.O. 1996, Re-examining the Cluster Hypothesis:Scatter/Gather on Retrieval Results, in Proceedings of the 19th annual InternationalACM SIGIR Conference on Research and Development in Information Retrieval, Zurich,Switzerland: ACM 1996.[13] Jansen, B.J. et al 1998, Real Life Information Retrieval: A Study of UserQueries on the Web, ACM SIGIR Forum: A Publication of the Special Interest Group onInformation Retrieval, 32(1): pages 5-17, 1998.[14] Enterprise Search Evaluation Guide, Seven critical characteristics yourenterprise search solution must have, 2006.[15] Tombros, A. and Ruthven, I. 1998, Advantages of query-biased summaries ininformation retrieval , in Proceedings of the 21st Annual International ACM SIGIRConference on Research and Development in Information Retrieval. Melbourne,Australia: ACM. 1998.ABOUT THE AUTHORSProf. Leon Rothkrantz, drs.dr., Faculty of Electrical Engineering, Mathematics andComputer Science, Man Machine Interaction Group, Delft University of Technology,Phone: +332787504, -Mail: firstname.lastname@example.com Defence Academy, Faculty Military Sciences, Den Helder, TheNetherlands.Maurice G. Wall, MSc, Faculty of Electrical Engineering, Mathematics andComputer Science, Man Machine Interaction Group, Delft University of Technology,Phone: +332787504, -Mail: firstname.lastname@example.com- 38 -
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INTRODUCTIONThe diffusion of information between individuals and in groups, or evenpopulations, can be considered a mayor stimulation for learning. While an informationtransmission from 1-to-1 or 1-to-n is a common method for initiating learning processes,e.g. teacher-to-class in school, the transmission from n-to-m in social networks may beassumed to be at least equally important. This is even more the case for e-learning,where participants activities are not bound by space or time, and/or in situations whichfocus more on knowledge generation than transmission. In both cases informationdiffusion is not only important for stimulating learning but for inter- and intra-groupcoordination and administration. Furthermore the simulation of information diffusion mayhelp to identify and select individuals for e-learning courses who will additionally spreadthe acquired knowledge in their social network vicinity. Thus reducing the number ofrequired training courses for an enterprise or organization. But not only does thesimulation of information diffusion may reap benefits for the customers side, it providesmayor advantages for the organizations or enterprises that offer e-learning courses, too.When considering and optimizing information flow, the learning and group experience ofall customers can be improved. Thus creating a good reputation and increasing theprobability of future customer contacts.SOCIAL NETWORKS AND GRAPH THEORYOne could define a network as a number of connected elements or entities. Whenthese connections resemble interactions or relations (e.g. kin- or friendships) suchnetworks might be called social networks. While the literature on social networks goesback at least to the work of Harary, Rapoport and others in the 1940s and 1950s [1], atypical analysis technique, the graph theory, even goes back to Leonhard Euler whodescribed in 1735 the problem of The Seven Bridges of Koenigsberg [2,3]. In graphtheory entities are called nodes or vertices and the connections edges [4]. Theseedges may have a certain weight attached, e.g. specifying the intensity of therelationship or the probability of an information transmission. Furthermore edges maybe either directed or undirected. Directed edges contain information from a certainperspective, e.g. who phoned or loves whom. Whereas undirected edges simply imply arelationship that is equal to both sides, e.g. kinships. The decision how to model therelationships between the entities depends on the objective of the network analysis.Coming back on the who phoned whom example, one might be inclined to model thiswith directed edges. While this is intuitive, it might not be required. This can beshowcased with the analysis objective of modelling the possibility of an informationdiffusion over synchronous communication channels in a social network. Here it mightbe suitable to model the edges as undirected relationships, as it can assumed that nomatter who initiated the communication, both participants may utter information.- 41 -
e-Learning09Considering the nodes themselves in information diffusion models there has beena certain lack of researching the effect of individual attributes. Exemplary for this is thesimulation of innovation diffusion, which we consider as a special case of informationdiffusion. Here it is common to propose that the acceptance of an information andafterwards its propagation is sufficiently modelled by assuming that the nodes onlypossess a certain threshold value [5]. When the ratio of connected nodes thatpropagate the information exceeds this threshold the node accepts the information andfrom now on transmits it, too. In more sophisticated models the individual thresholdvalues may vary belonging to one class, e.g. early adopter or laggard. Furthermore anadditional value for individual versus social preferences is introduced and the modellingof nodes in a regular lattice has been abandoned in favour of a scale-free network [6]. Insuch networks, the number of connections per node may vary. In another publicationwhere the spreading of information in a mobile phone network is considered, the nodesdo not even possess a threshold [7].The absence of further consideration of the nodes attributes in informationdiffusion models may be explained by the respective network research objective ofbasic statistical network properties. In contrast to that, recent publications researchingpositive and adversarial network effects on individual and group performance havestarted considering the influence of personality traits [8], too.THE FIVE-FACTORS OF PERSONALITYSome personality attributes can be considered more or less stable over a longperiod of time and are thus called personality traits [9]. One approach describes fivefactors or dimensions where each encompasses a large number of attributes. A modelusing this approach is the five-factor model of personality [10] another is often referredto as the Big Five [11]. Although both approaches have distinct origins, one beingderived from questionnaires the other lexically, they yield largely consonant models[12]. The results propose categories or domains that cover the personality of individuals.Thesedomainsarecommonlycalled:extraversion,agreeableness,conscientiousness, emotional stability and openness to experience. Following wewill introduce the five factors, mainly following [8,10,11,12], and propose how these mayinfluence the information diffusion in social networks.OPENNESS TO EXPERIENCEThe domain openness encompasses personality traits like active imagination,preference for variety, intellectual curiosity, but also non-conformity and autonomy. Thusit may be assumed that people scoring high on questionnaires referring to opennesswould have a lower acceptance barrier for new information and thus will accept thosewith a higher probability(I). The lowering of the acceptance barrier does not necessarilymean accepting the new information as the information will very probable be only one ofmany, possibly contradicting, information available to this person. In equation (I) prepresents the maximum influence of the openness trait on the message acceptancebarrier. The parameter N models the scale of the openness trait and x theparticipants score. Commonly the scale of the five-factors questionnaires reach from 0to 100, but this is not mandatory thus we modelled the scale as the parameter N. Incontrast to people scoring high on openness, people scoring low prefer familiarity overnovelty. Thus they probably resist information that does not match their view of theworld and may even hinder the flow of information that contradicts and foster the flow ofinformation that corresponds to their own world view(II).
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In equation (II) we again use the parameter p that signifies the maximum influencebut this time of the influence on the probability of information propagation. Theparameter x denotes the degree of accordance of an information to the world view ofthe participant. While d denotes the distance of positive and negative boundary valuesto the midpoint, in scales with an equal number of positive and negative options. Whenconsidering the accordance of an information item to the world view of a node rangingbetween absolutely non-conform to absolutely conform and having additionally oneoption for moderately (non-)conform the value for the distance d would be 2. Theparameter s represents the percentage score on a personality trait in thequestionnaire. Thus (1-s) signifies the fact that the influence on fostering or hinderingthe flow of information is stronger for individuals scoring low on openness to experience.(II)
AGREEABLENESSThe domain agreeableness encompasses personality traits like being generous,help- and trustful, cooperative and striving for social harmony. Thus people scoring highon agreeableness will probably not hinder information diffusion. Quite contrary theymight even invest by actively querying information from their many connections whenasked by another person, thus increasing the potential value of the propagatedinformation(III). In equation (III) we describe this value increase in relation to themaximum influence p, the scale of the questionnaire N and x the score of the individualin the agreeableness domain.(III)
Furthermore one can assume that information will be less scrutinized and thusmore easily accepted. This can be modelled by reusing equation (I). Whereas peoplescoring low on agreeableness, are said to be manipulative in using their socialrelationships thus they might try to influence the flow of information to their ownadvantage. For example they might exaggerate (IV) or understate (V) the importance ofan information item to the receiver hoping either to retain or gain such an advantage.- 43 -
e-Learning09(IV)(V)Fig.4: Eqs. (IV) and (V) with exemplaryvalues for parameters.
CONSCIENTIOUSNESSThe domain of conscientiousness encompasses personality traits like beingcareful, self-disciplined, meticulous, organized, reliable and goal oriented. Thus it canbe assumed that people scoring high on conscientiousness might inhibit the flow ofinformation that might be considered rumour/unreliable or just because an informationitem is deemed insignificant by them. People scoring low on conscientiousness incontrast might inhibit the flow of information unintended due to being less organized,thus being prone to simply forget to tell somebody else. These two facts are describedby equation (VI).(VI)
The being seen as meticulous and reliable may have the additional effect, that incase of publishing information connected nodes will value those to a greater extent thanfrom other, less conscientious nodes. As these, less conscientious, nodes will be seenas unreliable the information from individuals scoring low will be devalued. Thevaluation of messages in relation to the level of conscientiousness of the sender isspecified by equation (VII).(VII)
EXTRAVERSIONThe domain of conscientiousness encompasses personality traits like beingsociable, gregarious, assertive, attention seeking, taking pleasure in extensive socialactivities while seeking less time to spent alone. People scoring high in extraversion willfoster the information flow due to their talkativeness and their many connections,increasing the possibility of a given message to be sent. To describe this fact we modifyequation (I) to (VIII). Furthermore when combined with a high agreeableness individualsmay invest even more time and resources in providing good information to nurture theirmany relationships. Nonetheless, in cases scoring extremely high, the talkativenessmight result in a decrease of the perceived value or importance by receiving nodes, as- 44 -
e-Learning09these may suspect that the sender only talks, probably making things up, to be in thespotlight(VIII).(VIII)
EMOTIONAL STABILITYThis domain is also inversely referred to as neuroticism and then encompassespersonality traits like being moody, tense, anxious and emotionally reactive. While whenbeing referred to as emotional stability it covers traits like being even-tempered. We willfocus in this work on emotional stability as people scoring low being easily brought outof their equilibrium by both bad and good news, while people scoring high are lessexcitable. Following this line of thought we can assume that low scoring people tend toincrease the flow of information that concerns themselves, of new things/ideas orexperience made. An good example for the latter would be an unsatisfactory contactwith a group member or e-learning tutor, where high scoring people might think nothingof it but people scoring low might boil with rage for days. Thus they can be assumed tospread a negative word-of-mouth for days, reducing group moral and performance. Incontrast to that emotionally stable individuals actually may inhibit the flow of informationas they might not get overly excited and thus find the information less interesting andassume that others share this opinion.CONCLUSIONS AND FUTURE WORKThe diffusion of information between individuals and in groups, or evenpopulations, can be considered a mayor stimulation for learning and influencesimportant aspects of e-learning like administration and individual/group performance.The simulations of such diffusion processes will render important insights and thus mayenable significant enhancements in a wide variety of e-learning concerns. We found thatin simulations the modelling set-ups for attributes of nodes and edges are commonlyrather simplistic and may thus influence the results negatively.In this paper we focused on the attributes of nodes and discussed the implicationsof the five-factors of personality traits on information flow and message valuation. Thefive factor approach was selected due to its general acceptance as a promising (thoughnot undisputed) approach [8,12] and as a compromise between modelling overlysimplistic and trying to model every single personality trait. Furthermore the five factors- 45 -
e-Learning09are referred to as the psychology of the stranger as they describe traits that are easilyperceivable but exclude non-public or context dependent aspects [13]. While thissounds like a disadvantage, for our project rather the opposite is true. As these traitscan be checked relatively easy using standard questionnaires it can be assumed thatreal instead of stochastic values can be used. While discussing the domains one mayhave thought that some aspects of personalities are modelled implicitly even insimplistic approaches. When thinking about agreeableness and extraversion one mayargue that these are implicitly modelled by the number or weight of edges a nodepossess, however this does not enable any prediction about the evaluation of messageimportance or selectiveness of nodes and neglects the other three dimensions. Thus wethink it important to model the nodes closer to reality. Needless to say further researchwill be necessary to analyse the prediction improvement when contrasted with simplisticor more complex set-ups.REFERENCES[1] Newman, M.E.J; Watts, D.J; Strogatz, S.H. Random graph models of socialnetworks. PNAS 2002 www.pnas.org/cgi/doi/10.1073/pnas.012582999[2] Euler, L.P. Commentarii academiae scientiarum Petropolitanae 8,1741,pp.128140[3] Euler Archive Retrieved: 01.06.2009 fromwww.math.dartmouth.edu/~euler/pages/E053.html[4] Kleinberg, J.M.; Easley, D. Graph Theory, Retrieved: 01.06.2009 fromwww.infosci.cornell.edu/courses/info204/2007sp/graphs.pdf[5] Solomon, S.; Weisbuch, G.; de Arcangelis, L.; Jan, N. and Stauffer, D.: SocialPercolation Models, Physica A, 277 (2000) 239-247[6] Delre, S.; Jager, W; Janssen, M.A. The influence of network topology andsocial preference on diffusion processes Retrieved: 05.06.2009 from www.unikoblenz.de/~essa/ESSA2004/files/papers/Delre_Jager_Janssen_M2M2.pdf[7] Barabsi A.-L. et al. Structure and tie strengths in mobile communicationnetworks PNAS 2007 www.pnas.org/cgi/doi/10.1073/pnas.0610245104[8] Xia, Ling.; Yuan, Y. Connie; Gay, Geri Exploring Negativ Group Dynamics:Adversarial Network, Personality and Performance in Project Groups Ret: 05.06.2009from http://www.allacademic.com/meta/p192590_index.html[9] Allport, G. The general and the unique in psychological science. Journal ofPersonality, 30, 405-421 (1962)[10] Digman, J.M. Personality structure: Emergence of the five-factor model.Annual Review of Psychology, 41(417-40) (1990)[11] Goldberg, L.R. The development of markers for the Big-Five factor structure.Psychological Assessment, 4(1), 26-42 (1992)[12] Srivastava, S. Measuring the Big Five Personality Factors. Retrieved:14.07.2009 from www.uoregon.edu/~sanjay/bigfive.html[13] McAdams, D. P. (1995). What do we know when we know a person? Journalof Personality, 63, 365-396ABOUT THE AUTHORSProf. Dr. Wladimir Bodrow, PhD, Business Informatics, University of AppliedSciences HTW-Berlin,Phone: +49 (030) 5019-2478, -mail: firstname.lastname@example.com. Markus Helfert, PhD, School of Computing, Dublin City University,Phone: +353 -1700 - 8727, E-mail: firstname.lastname@example.com Steinicke, BSc, University of Applied Sciences HTW-Berlin,-mail: email@example.com- 46 -
INTRODUCTIONDuring the last decade the information and communication technologies (ICT)have become a crucial element of the university educational environment, as underlinedby all key research documents of the European Union (2008). At the same time, the EClast report on e-learning registers a delay of this sector regarding the exploitation of thefull potential of the ICT in the process of transforming the teaching and learningprocess, and in the redesign of curricula and modules.Other reputable investigations in the field note, that regardless of the potential ofthe technology for revolutionary transformation of the education, the dominating learningmodel worldwide has not yet sufficiently changed (Garrison D.R. and T. Anderson,2003).That is why experts attention has shifted from the ICT accessibility towards theproblem of their ability to transform the learning process. Considering the enormousamount of information at our disposal, and technologies for its storage, processing andtransfer, one of the most important questions now is the one about the most efficientway of transforming information for individuals personal use. This process is nottechnological by nature, but rather pedagogical physiological and social.1. CONSTRUCTIVIST IDEAS AS AN E-LEARNING METHODOLOGYContemporary research clearly shows that the dominating theoretical andmethodological platform underpinning e-learning throughout the world is constructivismwith the theoretical variations it possesses and their practical application.One of these variations is the communal constructivism. It is focused onconstructing the learning experience in a way to create an opportunity for distributedcognition forming and functioning. As defined by B. Holmes and J. Gardner (2006),communal constructivism is an approach to learning in which students not onlyconstruct their own knowledge (constructivism) as a result of interacting with theirenvironment (social constructivism), but are also actively engaged in the process ofconstructing knowledge for their learning community. (Holmes and Gardner, 2006)Salomon and Perkins note that the distributed cognition is characteristic forlearning to learnfrom the others, to learn with them, and to learn to facilitate theirlearning not only because of them, but also because of the contribution you make to thecollective knowledge The contribution to the collective knowledge most probably willlead to enhancing the personal knowledge as well.( Salomon and Perkins, 1998)Communal constructivism is characterized by the integration and symbiosis of thetendencies in learning theories development and the development of technologies. Inother words, the appearance of new technologies and the possibility to integrate them ineducation create new variants of the learning experience, respectively new approaches- 47 -
e-Learning09to their theoretical description. Thus theoretical and practical premises for enriching thesocial constructivism contents are created, which lead to its transformation intocommunal constructivism. The latter exists thanks to the appearance of e-learningenvironments, facilitating the communication and collective work of many users, withoutwhich its practical application is impossible. It is virtually the only variant of theconstructivist paradigm whose existence depends on certain level of technologydevelopment.A learning environment designed by the communal constructivist model accordingto Holmes and Gardner (2006) has the following features in comparison with thetraditional,: The ideas of the learners are subject to investigation, the artifacts they produceare publically available, and are used to produce other, more sophisticated artifacts; The collective knowledge is of importance. The goal of the community is thepermanent improvement, which requires investigation on the knowledge of theindividual, followed by further development of this knowledge. The responsibility for learning is transferred to the learner. Students receive partof the responsibility for planning, managing, asking questions, and systematizingknowledge for the commune. These activities are part of the teachers functions in thetraditional classroom. The teacher in this model has the role of a learning expert andguides the practice of enriching communal knowledge.2. COURSE DESIGN FOR ICT IN SOCIAL WORKThe characteristics from above were used to support the design of a course in ICTin Social Work, whose impact on the student learning experience was investigated. Thispaper presents the analysis of the data collected.ICT in Social Work is a compulsory discipline for the students of Social Workspecialty at Sofia University, Faculty of Education. The number of students varies fromyear to year and is between 40 and 60. The classes are 45 lectures and 45 seminars.An important characteristics of the indicative content is that it is dynamic,contextually determined, undergoing constant changes in regards with the developmentof the contemporary technology and the diversity of social work contexts, in which theyare integrated. Therefore it is very suitable for a course based on communalconstructivism, in which the students are co-authors of the content.Macro-design Level (H. Beetham, 2005)The course can be described as a blended-learning type as part of the activitiesare performed in a virtual learning environment (VLE) Moodle1, and part are in atraditional classroom.The aim is students to develop knowledge about ICT and skills to use them, whichto effectively apply in diverse professional context.Activity management: students work in small groups of 4 to 5 people on a topicchosen in advance from a list of topics, they are also given the choice to formulateadditional topic whose relevance and volume must be confirmed by the tutor.Envisioned results: students work on a group project. The process consists ofseveral stages: 1) investigating information resources on a given topic using traditionaland online sources, developing the project structure with a reliable and up-to-datebibliography; 2) designing tools for researching opinion and conducting a research; 3)developing the topic and the presentation, and presenting the latter before audience.The presentation includes the result of research.The assignments with the resources, as well as the presentations, are to be1
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e-Learning09uploaded in the VLE and are used by all students as sources for the final essay.Micro-design level block of activities introductory and information. Dominated by the traditionalmethods of teaching and learning (the first 5 weeks of the semester) block of activities development of group projects. Characterized by balancebetween traditional and online modes of learning and student support. Use of the VLEfor some of the activities. block of activities dominated by online communication during the last stageof the course, with discussions as a main learning activity. All resources developed bythe students and tutors are available for all students in order to support their preparationfor the final essay on the course content.3. THE CASE-STUDY: INVESTIGATING STUDENT OPINIONS ANDATTITUDES TOWARDS THE COURSE KEY ELEMENTS AS WELL AS THEDESIGN AS A WHOLEThe investigation on student opinion and attitude towards e-learning activities,their advantages and disadvantages over the traditional ones, towards elements of thecourse design and the overall educational efficacy of the course was conducted via aquestionnaire, based on open and closed questions. The opinion of all 57 students whotook the course during the summer semester of the academic 2007/2008 wasresearched on. The following conclusions and summary were drawn on the basis of thisdata analysis:Most of the students evaluate extremely positively the overall quality of the course:more than 80% define it as excellent and very good.Student opinion and their grading of the different elements of the constructivistlearning design reflect the high level of realization of the model. For example, 84% ofthe respondents are positive about the way the tutors team motivates them to do theirbest. They agree that this course indeed tries to make them do as much as possible(75%) and encourages them to develop their personal academic interest (67%). Theknowledge and skills gained through the course have a great value for the futureprofessional realization for 77% of the respondents.Student opinion and attitudes towards the key characteristics of the designfeaturing constructivism and communal constructivism in particular:Indicative content characteristics: flexibility and student choiceOne of the key elements of the constructivist design is flexible indicative contentand an opportunity for the students to choose content and tasks to be performed.Students evaluate this element of the course as follows: 57% of the students believethat the course gives the opportunity to choose contents in the given field ofknowledge, and 53% believe that they are given a lot of freedom to choose tasks to bedone. However, when analyzing these data should be taken into consideration thataccording to research on student learning styles a sufficient number of the studentswould prefer not to have such a choice and to receive strictly defined tasks with wellstructured content.For example, the question Which of the statements best suit your preferredlearning strategy? elicited 72% of responses under the well-structured content. 54%would use authentic sources. This fact could be explained by the dominatingtransmission teaching model in Bulgarian higher education, yet it is clearly a matter of alearning style as well. Therefore it is imperative to consider a balanced approachconcerning the flexibility of the content and the choice of tasks in order to meet studentneeds.-
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e-Learning09Students as co-designers of the course contentThe question What do you think of a course design in which students create partof the content through project development and presentation before an audience?elicited ranking of the potential advantages. The answers could be seen in table 1.-
It can be concluded that the strongest sides of such a design are related to theactive internalization, the opportunities for student self-expression, and the passiveknowledge gaining. Notably, the students dont think that tutors job is to deliver lecturesand students have to study them.These positive numbers lead to the conclusion that such a design has a positiveimpact on student learning.Stimulating the individualized approaches to learning and studentcreativityConstructivist design ambitions to stimulate the individualized approaches tolearning and student creativity were checked through statements such as Studentshave a substantial choice about how to study in this course and All I need in order tosucceed in this course is good memory. The former statement elicited 58% ofagreement, and the latter - 62% of disagreement. It can be concluded that the majorityof the students believe that they have a choice about how to study, and that theirperformance does not depend on memorizing the contents.As for the individualization of the learning, a most of the students (69%) think thatThe tutors put a lot of effort in identifying the difficulties, which the students mayencounter during their study and offer individualized support (89%).-
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e-Learning09and comparison with the others, acknowledgement and feedback, etc. it can beconcluded that the advantages of this design have educational characteristics and arerelated to the substance of learning and its efficiency for each student.Table 2. Student open question answers: advantages of publishing student materialsDevelopment of ICT skillsDiscussions and commentsFast information retrieval (time-saving)AcknowledgementCompatibility, economy, comfort, opportunity to performCompetitiveness, well structured, gaining knowledge and skillsActivity stimulation during the lectures, exchange of experiences, interestBeing informed, choice, availability, no time restrictions, collaboration
The analysis of the negative sides of publishing students materials showed thattheir nature is rather technical, technological and ethical than pedagogical. As it can beobserved in table 3 the most influential negative side is the opportunities for plagiarismand use of unreliable sources. Some of these disadvantages can be used by the tutorsas a stimulating tool to encourage students to present better works: essays, reports,presentations. This can be done through the chance for all to comment (and criticize) awork once it has been published. The answers also show that the VLE makes studentsincompetence and lack of information; a fact which can be used as a stimuli for betterperformance by itself.Table 3. Student open question answers: disadvantages of publishing student materialsPlagiarismUnreliable topics / informationLack of InternetCompetitionTechnical problemsDifferences in the software usedTopics with incorrect contentUnscrupulous colleagues can change the informationCompulsory participationIlliteracyLimitation to publish on the topics onlyDependence on third partiessubjectivityFree upload
CONCLUSIONSIt is evident that ICT cannot change the learning nature, as made clear by theresearch so far. Yet, in the good tutor designers hands they are a reliable too fordesigning a flexible and adaptive environment, which to become a precondition forfacilitating learning in all students, regardless of their background knowledge, skills andcompetences, learning styles, motivation, and needs. A design, based on the communalconstructivist ideas is one of the possible approaches to promoting such environments.It is also important to say that there is no universally good e-learning course atuniversity level as the variables of the system, called education, are many and incomplex interrelationships. Only the tutor can have an overview of them all the time,and to adapt the design according to their dynamics. What is important for the success- 51 -
e-Learning09of every course is that it has to be carefully designed and thought over in advance, to beapplied flexibly, i.e. to be open for re-design, and to maintain constant student feedbackabout the design elements on the efficiency of their study. Following these directions toa great extent guarantees design efficacy, directed to supporting students in a studentoriented learning. ICT can be a reliable tool for achieving this efficacy.REFERENCES[1] Beetham, H. (2005) Review: developing e-learning models for the JISCPractitioner communities, http://www.jisc.ac.uk[2] Garrison D.R. and T. Anderson, (2003). E-learning in the 21st century,RoutledgeFalmer[3] Holmes, B. and Gardner, J. (2006) e-Learning. Concepts and practice. SAGEPublications[4] Salomon, G. and Perkins, D. (1998). Individual and Social Aspects ofLearning, Review of Research in Education, Volume 23, Available athttp://construct.haifa.ac.il/~gsalomon/indsoc.htm[5] The use of ICT to support innovation and lifelong learning for all A reportprogress, Commission staff working document, Brussels, 09/10/2008, SEC(2008)2629final, http://www.ifap.ru/pr/2008/n081201c.pdf, . 8ABOUT THE AUTHORAssoc.Prof. Roumiana Peytcheva-Forsyth, PhD, Faculty of Education, SofiaUniversity, Phone: +359 896 725060, -mail: email@example.com
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INTRODUCTIONChecking the identity of the user in e-learning systems is designed only toidentification and authentication of the user at the beginning of e-learning process. Theusers can share the identity in systems by means of sharing the username and thepassword. The introduction of additional authentication techniques by modelling userbehaviour characteristic provides an added level of security.This paper is organized as follows: Section 1 describes the related works andSection 2. is about the our solution of modelling user behaviour, proposed algorithm, itsimplementation and Section 3 is about the testing and evaluations and the final sectionpresents conclusions and suggestions for future work.1. RELATED WORKIt was shown [3] that the same neurophysiological factors that shape our writingactivities, and make it unique, are also responsible for the rhythm and dynamics ofwriting on the keyboard. It is a sign that it is for each individual characteristic and hardlyimitable. For this reason, the model of keystroke dynamics provides of interestingopportunities in the field of electronic systems. We analyze previous work [5] on thetopic keystroke dynamics, we analyze the type of used measures, the evaluationmethods and the data used for testing. Some authors uses the statistics evaluationmethods [2], [13] and some authors used as evaluation method the neural networks[18], [19]. The measure for keystroke dynamics are digraphs, trigraphs, flight time anddwell time. Mouse dynamics can be used for user identification [1] too. For thischaracteristic are use the mouse actions in the categories: mouse-move general mousemovement, drag-and-drop the action starts with mouse button down, movement, andthen mouse button up, point-and-click mouse movement followed by a click or a doubleclick, and silence no movement. This is a user behaviour on lower level, which ismonitored the keystroke dynamics and mouse movement.We can model user behaviour on higher level, which is based on his action incomputer system. We analyze previous work to identified user behaviour patterns.Interaction between user and program can be analyze on the several levels of theabstraction [10], [11], from the lower level where are physical events to the higher levellike windows system events, applications level, The monitoring user work style andidentifying different episode from the users actions [16] should by used for intelligentadaptive user interface.2. OUR SOLUTIONWe present the system for passive authentication of user, which is based onmonitoring the events generated by operating system in response to user activity withinthe scope of arbitrary program. Our solution is based on analyze of the research [10],[11] and [16] and we propose the monitoring user work style without the time. Our- 53 -
e-Learning09system process and evaluate the events in several steps, see Fig. 1. We haveimplemented the modules for the capture events generated by the operating systemusing the Windows API libraries, a module for processing these events, a module forcreating a user model, and finally evaluation module.Eventsgeneratedby OS
Capture events
Data file
Creation of usermodel
Result ofevaluation
User model
Fig.1. The diagram of propose system for modelling user behaviour characteristicsfor identificationWe propose 3 comparative algorithms.We propose comparative algorithm which is based on percentage of appearanceevents in patterns and events on the all user models. The winner is user model which isclosest to the compatible model (our algorithm 2) [17].winner =Max{ each m in M : NEPEMm/NEP*100% }
Legend of (1):NEPEM number of events in pattern which is equal to the model m,NEP number of all events in pattern,M set of all users models.The model is created from user events generated by the operating system inresponse to user actions. Events are stored in the model as a unique set of eventstogether with their frequency, which determines how often the event is generated in the- 54 -
e-Learning09work of a particular user. Model also includes basic information about the user whocreated it in addition to the events.The module for capture events generated by the OS, logging module isimplemented like a applications which generated a data file with system events. In thetable 1 we can see the list of attributes monitoring events.The systems events for monitoring: events applicable for higher level of user monitoring:o Accelerator event (A),o Control event (C),o Key event (K),o Language change event (LC),o Menu action event (MeA),o Menu select event (MS),o Sys command event (SC),o Window activated event (WA),o Window created event (WC),o Window destroyed event (WD), events applicable for lower level of user monitoring:o Key event (K),o Mouse action event (MoA),o Mouse move event (MM),o Mouse wheel event (MW).Table 1: The list of attributes monitored eventsEventAttribute
Event typeName of the processTimeID componentTagsKeyAllocation of keys.CoordinatesIdentifier
A C K LC MeA MS MoA MM MW SC WA WC WD
3. EVALUATIONWe are using for testing and evaluating [17] the data from 22 users, what is 471hours of active work with computer in operating system. The data were first filtered toinclude only higher level events. Subsequently was all files distributed in the samples,while the limit for the distribution we used the number of events.Characteristics for evaluation of model:y FRR, system do not authenticated authorized user (FRR - False RejectionRate, it also uses the term False Alarm Rate), error type 1 - a legitimate useris rejected,y FAR, system authenticated impostor as an authorized user (FAR - FalseAcceptance Rate, also used the term Impostor Pass Rate), error type 2 - animpostor is accepted as a legitimate user."For evaluating the our algorithm 2 we choice the threshold in range 50-80%. Allcalculated values FAR and FRR are on figure 2 and 3.- 55 -
e-Learning09We managed to obtain sufficient amount of data, but not all users to providesufficient amount of data to create a stable model of the user. This resulted in highlevels of FRR in the number of users 11,13,18,20.
Fig.2. Calculated values FRR and FAR for threshold 50% - 80%, algorithm 2
Fig.3. Calculated values FRR and FAR for users, threshold is 74%, pattern 300events, algorithm 2The results of our test shows that the algorithm 2 is capable of when the samplesize of 100 events with relatively high accuracy to identify the user when the values ofFRR and FAR 3.077% 2.289%. Please note that 100 events will generate an averageuser in the active work time for 1-2 minutes.The constrains of our systems are in knowledgeable mimicking of user behaviourin systems and in changing of user environment - operating systems, software.One of the main benefits of this work is to confirm the ability to identify the userbased on his behavior at a higher level. For verification of the proposed solution hasbeen shown that using high-level model of the user is able, with relatively high precision(FRR = 3.66% and FAR = 1.47%) to determine whether the computer works authorizeduser or an attacker. Important role in identifying and selecting appropriate play features(in our case, events) on the basis of which it is possible to distinguish the behaviour ofmultiple users.Another benefit of high-level modelling in comparison with the modelling at thelower level is to improve the reaction time of the security system. In the case of writingon the keyboard this time around about 10 minutes [9], and we must take into accountthe fact that the user must use the keyboard at work. A similar situation is also in thecase of work with the mouse, where you can calculate response time moving beyond- 56 -
e-Learning09the 13 minute [1], that we know with reasonable accuracy to identify the user. Again it isa condition that the user must work with the mouse. The advantage of the solutionproposed in this work is the fact that the data are obtained simultaneously from both theinput devices, allowing to reduce the response time to a level of 1-2 minutes (dependingon the intensity of the work of the user) while maintaining acceptable levels FRR =3.077% and FAR = 2.289%.CONCLUSIONS AND FUTURE WORKWe propose a method for passive authentication of user when use the computersystems during e-learning process. Similar models can be employed in e-learningenvironments [12]. Module was implemented for the capture events generated by theoperating system using the Windows API libraries, a module for processing theseevents, a module for creating a user model, and finally evaluation module. To verify theidentity of the user in the evaluation module, we designed and tested 3 methods ofcomparison samples of compliance with the model. Verification of the proposed solutionwe make to set of 22 users, from which we obtained a total of 736 hours of work for acomputer, which is about 1.14 * 106 high-level events. The effectiveness of differentmethods of comparison, we verify the calculated values FRR (number of authorizeduser rejection expressed in percentages) and FAR (the number of penetrations attackeras a percentage) for all 22 users. For best results we achieved user authentication withcomparing, with which we are in the size of the sample 300 events measured valueFRR = 3.66% and FAR = 1.47%, with 300 events in the active user will generate workfor about 3-6 minutes.For the above limitations and benefits that better results could be achieved bycombining the two levels (upper and lower) and thus created a model of behavior, whichwould use the advantages of both approaches. The connection could it be implementedin several ways: combination of different models keystroke dynamics, mousemovements and users behaviour on higher level using systems.ACKNOWLEDGEMENTThis work was partially supported by the Scientific Grant Agency of the SlovakRepublic, grant No. VEGA 1/0508/09.REFERENCES[1] Ahmed A. E. A., Traore I. A New Biometric Technology Based on MouseDynamics, IEEE Transactions on Dependable and Secure Computing, July-September2007,vol. 4, no. 3, pp. 165-179.[2] Bergadano, F., Gunetti, D., and Picardi, C. User authentication throughkeystroke dynamics. ACM Trans. Inf. Syst. Secur. 5, 4, Nov. 2002, 367-397.[3] Bryan, W. L., Halter, N. Studies in the physiology and psychology of thetelegraphic language. In: Gardener, E. H., Gardner, J. K.: The psychology of skill: Threestudies. NY Time Co., NY 1973. s. 35-44. As consistent with: Obaidad, M. S., Sadoun,B.: Keystroke dynamics based authentication. In: Jain, A. K., Bolle, R., Pankanti, S.:Biometrics: Personal identification in networked society. IBM T. J. Watson ResearchCenter, Yorktown Heights, NY, 1998, s.213-230[4] Chandra, A. and Calderon, T. Challenges and constraints to the diffusion ofbiometrics in information systems. Commun. ACM 48, 12 , Dec. 2005, 101-106.[5] Chud, D., urfina, M. Multifactor Authentication based on keystroke dynamicsIn: Compsystech 2009, Rousse, Bulgaria, 2009.[6] Costa, P. C. G., Barbar, D., Laskey, K. B. et al. DTB Project: A BehavioralModel for Detecting Insider Threats. International Conference on Intelligence Analysis,2005,- 57 -
e-Learning09[7] Klein, D. V. Foiling the cracker: A survey of, and improvements to, passwordsecurity. In Proceedings of the 2nd USENIX Security Workshop, pages 514, August1990.[8] Enhanced Online Banking Security - Zero Touch Multi-Factor Authentication.Entrust, Inc.[9] Gunetti, D. and Picardi, C. Keystroke analysis of free text. ACM Trans. Inf. Syst.Secur. 8, 3 , Aug. 2005, 312-347.[10] Hilbert, D. M. and Redmiles, D. F. Extracting usability information from userinterface events. ACM Comput. Surv. 32, 4 , Dec. 2000, 384-421.[11] Hilbert, D.M., Robbins, J.E., and Redmiles, D.F. Supporting Ongoing UserInvolvement in Development via Expectation-Driven Event Monitoring. Tech ReportUCI-ICS-97-19, Department of Information and Computer Science, University ofCalifornia, Irvine, April 1997.[12] Ivanovi, M., Pribela, I., Vesin, B., Budimac, Z: Multifunctional environmentfor e-learning purposes, Journal of Mathematics, novi Sad, vol.38, br. 2, str. 153-170,2008.[13] Joyce, R. and Gupta, G. 1990. Identity authentication based on keystrokelatencies.Commun.ACM33,2,Feb.1990,168-176.URL:http://doi.acm.org/10.1145/75577.75582[14] Lammers, A. and Langenfeld, S.: Identity Authentication Based on KeystrokeLatencies Using Neural Networks. J. Comput. Small Coll. 6, 5 (Apr. 1991), 48-51.[15] Lee, J., Choi, S., and Moon, B.: An evolutionary keystroke authenticationbased on ellipsoidal hypothesis space. In: Proceedings of the 9th Annual Conference onGenetic and Evolutionary Computation (London, England, July 07 - 11, 2007). GECCO'07. ACM, New York, NY, 2090-2097.[16] Liu, J., Wong, C. K., Hui, K. K.: An adaptive user interface based onpersonalized learning. In: IEEE Intelligent Systems, Mar.-Apr. 2003, s. 52-57.[17] Majerk M.: Model pouvatea pre jeho identifikciu (), In: diploma work,Faculty of Informatics and Information Technologies, Slovak university of technology inBratislava, may 2009[18] Obaidat, M. S., Sadoun, B.: An Evaluation Simulation Study of Neural NetworkParadigm for Computer Users Identification. Information Sciences Journal-Applications,Elsevier, November 1997, vol. 102, no. 1-4, pp. 239-258.[19] Obaidat, M.S., Macchairolo, D.T.: An On-line Neural Network System forComputer Access Security. IEEE Trans. Industrial Electronics, April 1993, Vol. 40, N. 2,pp. 235-241.[20] Schneier, B.: Sensible Authentication. Queue 1, 10 (Feb. 2004), 74-78.[21] Urnphress, D., and Williams, G.: Identity verification through keyboardcharacteristics. Int. J. Man-Machine Studies 23, 3 (Sept. 1985), 263-273ABOUT THE AUTHORSMgr. Daniela Chud, PhD., Institute of Informatics and Software Engineering,Faculty of Informatics and Information Technology, Slovak University of Technology,Ilkoviova 3, 842 16 Bratislava 5, Slovak Republic, -mail: firstname.lastname@example.com. Maro Majerk, graduated at Faculty of Informatics and InformationTechnology, Slovak University of Technology in Bratislava, Ilkoviova 3, 842 16Bratislava 5, Slovak Republic, -mail: firstname.lastname@example.com
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INTRODUCTIONMachine perception can make Human-Computer Interaction more natural, imitatingthe ways we usually interact each other in interpersonal communication. If this is true ingeneral, the e-learning sphere can particularly benefit from perceptive capabilities: bybecoming more human-like, a computer able to recognize what users do or whathappens around them may also improve the learning process, through theenhancement of interaction procedures [2].According to the World Health Organization, about six hundred million persons inthe world are affected by disabilities of some kind, due to several causes [14]. Even ifnot all of these people have disabilities impeding computer or Internet access, it ishowever a great part of the world population. Nevertheless, nowadays practically allexisting e-learning systems can only be navigated with the mouse, and arecharacterized by several other accessibility lacks. In the context of InformationTechnology, Accessibility deals with issues like these, to reduce (or even eliminate) anygaps between healthy and disabled people in information access. On the Europeanside, the European Commission strongly promotes several initiatives aboutAccessibility. For instance, eAccessibility within the e-Inclusion activity aims atensuring people with disabilities and elderly people access ICTs on an equal basis withothers. Also, the new i2010 initiative on e-Inclusion (To be part of the InformationSociety") includes a strategy targeted on enhancing accessibility to the InformationSociety for all potentially disadvantaged groups [3].In recent years, eye tracking systems [4] have greatly improved, beginning to playan important role in the assistive technology field. Eye tracking relates to the capabilityof some devices to detect and measure eye movements, with the aim to preciselyidentify the users gaze direction (usually on a screen). The acquired data are thenrecorded for subsequent use, or directly exploited to provide commands to thecomputer. The eye tracker we use in our laboratory the Tobii 1750, one of the mostwidespread eye tracking devices combines video-oculography with infrared lightreflection. The system, which looks like a common LCD screen, is provided with fiveNIR-LED and an infrared CCD camera, integrated in the monitor case. Infrared lightgenerates corneal reflections whose locations are connected to gaze direction. Eyepositions are recorded at a frequency of 50 Hz, and with a precision of about 0.5 of thevisual field.Eye tracking can be profitably exploited for evaluation purposes, allowing a greatamount of data about users behaviours to be obtained for example, while interactingwith e-learning systems. Thanks to the acquired information, users gaze paths can be- 59 -
e-Learning09reconstructed, and possible focuses of attention identified. Analogously, eye trackingtechniques are also valuable for advertising and marketing purposes.However, it is when eye tracking is used as a direct input source for the computerthat assistive interfaces can be built. These tools can significantly improve theaccessibility of software interfaces, making them also usable by disabled people. Usinga keyboard and a mouse is in fact impossible in all those cases where serious motorimpairments both temporary and permanent impede easy hand movements. Inthese situations, the eyes may be the only communication channel for an individual.Several eye-controlled systems have been developed to date, but there are stillsome open questions connected with human-computer interaction, and withaccessibility in particular. Our activity is aimed at creating more accessible and usableinterfaces based on eye tracking, which could make e-learning really available toeverybody.SOME EYE TRACKING PROJECTS AT THE UNIVERSITY OF PAVIAEye-S: a Full-Screen Input Modality for Pure Eye-based CommunicationEye-S is a system for pure eye-based communication, allowing the input of bothtext and generic commands [12]. E-learning systems often require some kind of inputfrom the user, which however cannot be provided by a severely-disabled person.The problem of writing through the eyes has been widely considered in the past,and a number of solutions have been proposed. The simplest approach is surely thatbased on on-screen keyboards and dwell time: if the user looks at a certain key formore than a predefined time interval, the key is considered pressed and thecorresponding letter is typed. Several studies have been carried out connected to thiskind of typing and related issues (e.g. [5] and [6], focused on the efficacy of dwell timeand the importance of feedback). There are also variants where the dwell time issubstituted with some kind of switches, such as physical buttons, eye blinking (e.g. [13])or facial muscle activation.In the context of action-based gaze input (for example to perform drag and dropoperations), Milekic [7] introduced the concept of eye graffiti, where gaze gestures areused to form a vocabulary in a way similar to the text input mechanism used in personalorganizers. In these devices, natural input is obtained by drawing letters, or parts ofthem, through a pen. Our work derives basically from a similar principle.Compared to other eye writing systems, Eye-S has a main advantage: it leaves thescreen totally free for the display of applications, since there is no need for specificgraphical input interfaces. Using an eye gesture approach, the user creates alphabetletters, as well as punctuation marks or specific commands definable according to theparticular application to control, by means of sequences of fixations on nine (hidden)predefined areas on the screen (Fig. 1a) squares (called hotspots) placed in easilyidentifiable positions, even if not explicitly displayed: the four vertices of the screen, themiddle of each side and the center.Normally, hotspots are not visible. However, their position is evident, and almostno effort is required to the user to remember where they are. This means that thescreen is totally available for displaying any content, and no area is occupied by specificinterfaces for text input.An eye sequence (from which the name Eye-S stems) is a succession of fixationson the hotspots. When the user looks at a hotspot for more than a given threshold (e.g.400 milliseconds), a sequence recognition process starts. If other hotspots are looked atafter the initial one within configurable time intervals, and if the succession of watchedhotspots pertains to a set of predefined sequences stored in a database, then acorresponding action is performed. If the system is being used for text input, the actionwill be the same as typing a key on a keyboard.- 60 -
e-Learning09Eye sequences can be chosen arbitrarily, but in the writing context they will ofcourse resemble the form of letters (possible eye sequences for the a and bcharacters are shown in Figure 1b).
a.b.Figure 1. Eye-S: (a) Explicit display of hotspots on the screen; and(b) Examples of eye sequences for the a and b lettersDuring system use, it is generally helpful to get a feedback about the sequencecomposition process. To this purpose, when the user looks at the first hotspot for morethan the defined dwell time, a small green square is displayed within the hotspot itself.Such square contains a 1, to indicate that this is the first hotspot of a possiblesequence. If the user looks at another hotspot within a timeout, then a yellow squareappears, with a 2 written inside it (and the green square is deleted). If the sequencewhich is being recognized is three segments long, the same happens for the thirdhotspot (orange square and 3 as a sequence indicator). At last, on the final hotspot ofa sequence whether it is three or four segments long a red square is displayedwhich contains the character or action recognized, in order for the user to immediatelyunderstand that the eye gesture has been successfully detected.WeyeB: an Eye-Controlled Web Browser Hands-Free NavigationWeyeB (from Web eye Browser) is an eye-controlled Web browser [11] enablingthe two basic activities required when surfing the Web, namely page scrolling and linkselection (a very common activity in e-learning systems, usually based on the Weband/or on hypermedia interaction styles).Page scrolling can be activated by simply looking at one of two buttons, placedabove and below the display area in the WeyeB interface. If the watching time (thedwell time) goes beyond a predefined value (e.g. 1 second), then the button isconsidered pressed. When this occurs, a semitransparent scroll rectangle is displayedover the page (Figure 2a). By looking within the scroll rectangle, the content can bescrolled, with a speed that is lower (or even null) when the users gaze is in the centralarea of the rectangle, and increases progressively as it moves towards the upper orlower edges. The scroll rectangle is displayed as long as the users gaze is detectedwithin it. Once the user looks anywhere outside the rectangle for more than a certaintime (2 seconds in our experiments), it disappears. This page scroll solution gives theuser full control: no scrolling can occur accidentally, since the scroll rectangle must bedisplayed beforehand, and the explicit display of such a graphical element representsan important visual feedback.For the selection of a link, the user must look at it for (at least) a dwell time.Subsequently, his or her gaze must be rapidly shifted upward, and then immediatelydownward, again (about) on the target link. This sort of eye gesture, unlike other- 61 -
e-Learning09techniques, does not interfere with normal reading activities.
a.b.Figure 2. WeyeB: (a) Scroll rectangle; and(b) Link menu displayed after the acquisition of the snapshotThe detection of a selection gesture causes a snapshot of the area around thelink to be acquired the precision of current eye trackers is in fact limited, and theperceived gaze may not be exactly centered on the link. The rectangular region (whichhas a fixed size, e.g. 200 x 100 pixels, and is centered on the fixation point) is stored inthe form of a bitmap image. Such image is then analyzed with OCR techniques toextract its textual content and identify possible (textual) links present in it (through acomparison with the content of all the <a> tags in the HTML code). If the snapshotcontains exactly one link, the corresponding page is loaded. If it includes more than onelink, a popup menu is shown which lists all the recognized links, easily selectable bygaze (Figure 2b). Lastly, if no textual links are identified in the snapshot (e.g. because itcontains an image link), the mouse cursor is simply shifted to the initial selection pointand a mouse click is generated.e5Learning: an E-Learning Environment Based on Eye Trackinge5Learning [1][10], whose name stems from enhanced exploitation of eyes foreffective eLearning, is an e-learning environment where eye tracking is exploited toallow the computer to get valuable data about users and their activities. Even if itsfunctions are not directly related to accessibility issues, e5Learning can significantlyimprove the quality of distance learning, which is often the only possibility for a disabledperson.The system is characterized by three key functionalities: (1) detection of basic useractivities, such as reading text and observing multimedia content, in order to maintain a"history" of user actions; (2) generation of additional content depending on the context(e.g. textual or multimedia descriptions shown when the user is reading a specificportion of text); (3) recognition of stress, high workload and tiredness states in the user,using physiological data obtainable from the eye tracker.Thanks to the Monitor of Accessed Screen Areas, the author of the course candecide how much attention the user should pay to certain portions of content. In ourprototype, a course is simply made up of web pages. We use an ad-hoc-built webbrowser which, along with page content, reads additional information defined by theauthor. Among other things, such information specifies the coordinates and sizes ofscreen rectangles (Regions of Interest, or RoIs) corresponding to relevant portions ofcontent, and associated data. The History Recorder submodule relies on the Monitor ofAccessed Screen Areas and keeps track of which portions of content (RoIs defined bythe author) have already been accessed by the user, as well as how much. If, forexample, in a previous session the user did not devote sufficient time to a certain area,its content might be subsequently proposed before others, independently of its position- 62 -
e-Learning09in the logical structure of the course. Another strategy, which is the one we haveactually implemented, explicitly highlights the regions which need attention. When theuser presses the next button to load the next page in the course, if in the current pagethere are ROIs that have not been fully read/observed, the system emphasizes themthrough colored rectangles (Fig. 3a).Using the Contextual Content Generator, the creator of the course can associatenew content to RoIs, and indicate the requirements for the additional information to bedisplayed (in the form of HTML pages appearing within a popup window, as shown inFigure 3b). A condition for the new window to be shown is that the fixation time within aRoI is higher than a threshold.A third module composing e5Learning is the Emotion Recognizer. Severalexperiments, mainly carried out in the Psychology and Physiology fields, havedemonstrated that the observation of eye behaviours can reveal much information aboutthe user emotional state. For instance, pupil size is significantly larger after highlyarousing stimuli than after neutral stimuli [9]. Other investigations suggest that themental workload can be assessed by analyzing the fluctuation rhythm of the pupil area[8]. In particular, in our project we have considered eye factors to (potentially) identifytwo main user conditions: (a) high workload or non understanding, and (b) tiredness.For instance, if the average pupil size has progressively increased within a certain timeinterval, also user workload may have augmented. A decreased blink rate in the sameperiod would further confirm such a supposition. When detected, these evidences couldbe used to dynamically modify the learning path, proposing a topic related to the mainone but less complex (a sort of break). Or, if the user is potentially having problems inunderstanding something, extra information may be displayed.
e-Learning09should become a priority in the near future: present technology is ripe to makeeducation really available to everybody.ACKNOWLEDGEMENTSThis work was supported by funds from the Italian FIRB project Software andCommunication Platforms for High-Performance Collaborative Grid (grantRBIN043TKY).REFERENCES[1] Calvi, C., Porta, M., Sacchi, D. e5Learning, an E-Learning Environment Basedon Eye Tracking. Proceedings of the 8th IEEE International Conference on AdvancedLearning Technologies (ICALT 2008), Santander, Spain, 2008.[2] Cantoni, V., Cellario, M., Porta, M. Perspectives and Challenges in E-Learning:Towards Natural Interaction Paradigms. Journal of Visual Languages and Computing,Elsevier Science Publishing, 15 (2004), pp. 333-345.[3] Communication "European i2010 initiative on e-Inclusion - to be part of theinformation society" Europes Information Society. Retrieved July 11, 2009, fromhttp://ec.europa.eu/information_society/activities/einclusion/bepartofit/overview/.[4] Duchowski, A. T. Eye Tracking Methodology Theory and Practice (2nd Ed.).London: Springer-Verlag, 2007.[5] Majaranta, P., Aula, A., Rih, K. Effects of Feed-back on Eye Typing with aShort Dwell Time. Proceedings of ETRA 2004, San Antonio, Texas, USA, March 22-24,2004, pp. 139-146.[6] Majaranta, P., Mackenzie, I. S., Rih, K. Using motion to guide the focus ofgaze during eye typing. Proceedings of 12th European Conference on Eye Movements(ECEM12), Dundee, Scotland, August, 2003.[7] Milekic, S. The More You Look the More You Get: Intention-Based InterfaceUsing Gaze Tracking. Proceedings of the 7th Annual Museum and the Web Conference,Charlotte, North Carolina, USA, March 19-22, 2003.[8] Murata, A., Iwase, H. Evaluation of Mental Workload by Fluctuation Analysis ofPupil Area. Proc. of the 20th Int. Conf. of the IEEE Eng. in Medicine and Biology, 1998.[9] Partala, T., Jokiniemi, M., Surakka, V. Pupillary Responses to EmotionallyProvocative Stimuli. Proc. of ETRA 2000, Palm Beach Gardens, FL, USA, 2000.[10] Porta, M. Implementing Eye-Based User-Aware E-Learning. Proceedings ofCHI 2008 Ext. Abstr., Florence, Italy, April 5-10, 2008.[11] Porta, M., Ravelli, A. WeyeB, an Eye-Controlled Web Browser for Hands-FreeNavigation. Proceedings of the 2nd IEEE International Conference on Human SystemInteraction (HSI 2009), Catania, Italy, May 21-23, 2009.[12] Porta, M., Turina, M. Eye-S: a Full-Screen Input Modality for Pure Eye-basedCommunication. Proceedings of the 5th Symposium on Eye Tracking Research &Applications (ETRA 2008), Savannah, GA, USA, March 26-28, 2008.[13] Rasmusson, D., Chappell, R., and Trego, M. Quick Glance: Eye TrackingAccess to the Windows95 Operating Environment. Proc. of 14th InternationalConference on Technology and Persons with Disabilities, Los Angeles, USA, March 1520, 1999.[14] World Health Organization. What is being done to improve the daily lives ofpeople with disabilities? Retrieved July 11, 2009, from http://www.who.int/features/qa/16/en/, 2008.ABOUT THE AUTHORSVirginio Cantoni, Marco Porta, Alice Ravarelli, Dipartimento di Informatica eSistemistica, Universit di Pavia, Italy. Phone: +39 0382 985 486, -mail:{virginio.cantoni, marco.porta, firstname.lastname@example.com.- 64 -
INTRODUCTIONDuring the past decades the trend of learning supporting media went from a strictinstructional design to Computer Based Training (CBT) and to Web Based Training(WBT). Today's claim of e-Learning is to achieve a maximum efficiency of knowledgetransfer with a minimum effort. Using the perspective and/or concepts of knowledgemanagement one can speak about the knowledge generation regarding the students.In the last years almost 200 LOs were produced within the scope of the CodewitzProject by several international teams. A detailed overview of the categories andnumbers of the produced LOs is available on the Internet site of the project [5].Characteristic for Codewitz Project is the fact that there exist only a few basic rules forthe design of the LOs which caused a huge variety of solutions that have beendeveloped. Besides implementing different user interfaces and having divergent levelsof user interaction all solutions have their specific understanding of knowledge transferand a corresponding learning philosophy. All the experience gathered by producing andusing these LOs need to be sorted and evaluated. Some of our evaluation resultsregarding in the project implemented LOs we presented before on several conferences[1, 2, 3, 4].Accepted and mainly applied in distance education today the concept of blendedlearning allows the usage of various content available on the WEB. Just because thegrowing part of it is the open source and can be used for free [6] we observe theoccurring wide integration of this open content into e-learning applications. But even ifone can find in Internet encyclopedia like Wikipedia [7] well described and presenteddocuments about all possible topics they are not a priori prepared/designed for learningas we defined it before for empowerment to solve tasks or problems in particularsituation or under special circumstances. Because of primarily orientation of Internetencyclopedia on scientific description of objects, events, processes, etc. they will nevercompete with FAQ which support the solution of real problems or tasks in the businessand in the everyday life. From our point of view the acceptable distance education ingeneral and e-Learning in particular has to combine both scientific and applicationoriented approaches. Especially important is it in such service oriented areas likeprogramming skills.2. LEARNING OBJECTS IN CODEWITZIn this chapter we will briefly introduce the typical learning objects developedwithin Codewitz. This selection represents only our view on more than 200 learning- 65 -
e-Learning09objects and just because of the continuously growing contributions to the project it is notcomprehensive for all LOs in the project.
Fig.1. Example 1.
Fig.2. Example 2.
Fig.3. Example 3.
Fig.4. Example 4.
Fig.5. Example 5.
Fig.6. Example 6.
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Fig.8. Example 7.
Fig.9. Example 8.
Fig.10. Example 9.
3. COMMON FEATURESThe analysis of pros and cons to presented LOs is reported in (Bodrow andBodrow, 2006). Here we will figure out the common features and attributes to be able tofind the similarities and discrepancies among these examples. These will be consideredas the foundation to develop the new and to evaluate the existing Learning objects.Firstly, the implemented LOs have very different look and feel: They differ in subdivision of the screen in number, size, and placement ofvarious windows, and in amount and quality of the content represented inthem; Just developed to support the improving of the programming skills, some ofthem contain no windows with the source code of presented example; Some examples use different colours and graphic attributes like curves andarrows, where another are implemented with strong reduced colour palette; Very different is the usage of screenshots and animations.Secondly, all (groups of) LOs follow different concept and corresponding structurefor explanation, navigation, and interaction: Some Learning objects implement the instructional concept, where the otherspresent the explanation of the knowledge to be submitted; Several solutions corresponds to constructivist approach and realize discoverylearning;- 67 -
e-Learning09The explanations in many LOs are implemented to be used in the class,whether another solutions are prepared for non-supervised learning; Some LOs offer the two-level-explanations (short and extended) for new andexperienced students. On the other hand, the short explanations couldpreferably be used in the class whereas the extended ones can be applied forthe homework; The navigation concept also differs from one LO to another. Some provideflexible change from point-to-point while others implement step-by-stepnavigation; Some of the LOs require user input like numbers, reserved words or multiplechoice selections.Thirdly, various (groups of) LOs are prepared for different utilization: Main part of the implemented Learning objects are prepared for theteaching/learning in the auditorium. The (very) short explanation on the screenand the step-by-step navigation are the typical attributes of such solutions; There are several Learning objects which implement the task-metaphor. Herethe students have to answer the question regarding the example presented onthe screen to receive the feedback; Some implementations involve the input of numbers to explain e.g. thenumber of repetitions in some loop construction another present predefinedexamples.Obviously the huge variety of LOs makes it difficult to figure out their comparableattributes. The analysis made allows the formulation of the following framework.
4. FRAMEWORKTo support the development of new and the evaluation of existing Learning objectsfollowing framework was defined:1. Thematic orientation2. Teaching/learning metaphor3. Content visualization conception4. Screen structure (corresponding to content visualization conception)5. Navigation model6. Interaction conceptAll these elements have to be considered together accompanied byinterdependencies to various environmental components. Because of the mentionedvariety of the conceptions and resources used in the developed Learning objects thederived framework is very general and could be specified. To present the way of thefurther specification for this framework the development scenario for the Learning objectwill be considered in the next chapter.5. DEVELOPMENT SCENARIOIn this chapter the scenario for the development of Learning object is presented.The most important decisions/steps are listed below including possible criteria. Decision about the topic/knowledge to be submitteda) Basic features/resources of programming languages (e.g. classes,objects, methods, variables, constants etc.);b) Standard concepts/algorithms with programming languages (e.g. loops,search or sorting algorithms, etc.);c) Special concepts/algorithms (graphical user interfaces, databaseprogramming, network applications, parallel computing, etc.); Decision about the metaphor for transferring the learning content:a) Supervised traditional classes versus non-supervised teaching/learning;b) Learning based on examples description;- 68 -
e-Learning09c) Problem oriented explanation with focus on common problems;d) Discovery learning (e.g. the exploration of the learning environment aspart of the learning process to support adaptation of the learning processto the user requirements); Decision about screen design and placement of the screen elements:a) Important windows (e.g. source code, variables, control windows, memory,explanation, screenshot etc.);b) Labels of the windows on the screen;c) Placement of the windows on the screen;d) Consequent usage of the layout concept;e) Highlighting important parts of the screen (e.g. by different colours andfonts, etc.); Decision about navigation within the LO:a) Flexible change from point to point, according to user prompt;b) Simple step-by-step navigation through the learning content;c) Hyperlinks (e.g. for additional information); Decision about interaction during the session:a) Input of source code elements;b) Input of numbers and parameters that change the execution and theoutcome of the LO;c) Selection of tf) Multiple choice test;g) Text input test;h) Test of reserved words;i) No test withind) he right answer (e.g. radio button); Testing the submitted knowledge:j) the LO (class based exams).This list as mentioned before is not exhaustive and represents the basic level ofthe framework.6. EVALUATION EXAMPLETo demonstrate the usage of this framework we exemplarily apply the criteria tothe before introduced LO example 6 from Codewitz shown in figures 6 and 7: Decision about the topic: The LO aims at supporting the understanding of database programming inJava. So it deals with special concepts of programming languages which requires thecomplex interaction of different resources. Metaphor for knowledge transfer: This example used the problem description at the beginning and theexplanation of selected examples in the main part. Generally this LO is designed to beused in non-supervised learning. Screen layout and usage The screen is clearly subdivided into a source code window on the left and abrief explanation on the right. Within this explanation window one can find hyperlinks toadditional information about the current line of code. These links open pop-up windowswith text and sometimes visualizations aids. This particular LO offers a second view on the learning content. Besides thebefore described window with source code and short explanation one can browse it bystarting an animation. The screen layout for this method of knowledge transfer alsocontains the source code on the left but on the right side it consists of several windowslabelled Explanation, Program, Java Resources, Program Resources, Database,- 69 -
e-Learning09and Screenshot. During the animation arrows underline the execution steps inselected lines. Navigation within the Learning object: The explanatory part of the LO empowers the user to navigate within thesource code by scrolling buttons in the middle of the screen. This enables the student tofocus on the lines of code to be learned in the session. It prevents from repetition ofunnecessary code explanation. The second part, the animation, offers simple start andstop control buttons. In this solution the user can follow both the step-by-step navigation as wellas the flexible and user oriented point to point browsing through the content. User interaction: In this LO there are no possibilities for the input of numbers or codefragments to change the execution or outcome of the example. Knowledge testing The LO contains no integrated tests of knowledge. For the usage asadditional teaching material it should be combined with class based examination.CONCLUSIONS AND FUTURE WORKThe proposed framework represents the basis for evaluation of developedLearning objects. In addition it can be utilized for the development of new solutions inthe Codewitz and similar projects. The detailed specification of the framework andfurther analysis of Learning objects is scheduled as the upcoming research activity.REFERENCES[1] Bodrow, W., Bodrow, I., Wolf, S., 2006. Didactics and Implementation ofContent for non-supervised Distance Learning. In: Tampere Polytechnic Univ. ofApplied Sciences, MMT2006 - Methods, Materials and Tools for ProgrammingEducation,pp.57-60,Tampere,http://www.codewitz.net/papers/MMT_5760_Bodrow_Wolf_Bodrow.pdf[2] Bodrow, W., Bodrow, I., 2006. Interaction Concepts for Learning Objects inCodewitz. In: e-learning conference '06 Computer Science Education, pp. 125 - 129,FCT (Fundao para a Cincia e a Tecnologia), Coimbra[3] Bodrow, W., Bodrow, I., 2007. Knowledge Engineering and Visualization forDistance Education in Programming. In: Proceedings of the 10th IASTED Conference inBeijing (CATE 2007) pp. 176 179, ACTA Press Zurich[4] Bodrow, W., Wolf, S., Bodrow, I., 2007. Challenge for the Learning Objects. In:R. Sadananda, E. Kujansuu (Eds.) Proceedings of the ITE2007, pp. 30 32, Bangkok[5] http://www.codewitz.net[6] http://www.microsoft.com/education/default.mspx[7] http://en.wikipedia.orgABOUT THE AUTHORSProf. Dr. Wladimir Bodrow, PhD, Business Informatics, University of AppliedSciences HTW-Berlin,Phone: +49 (030) 5019-2478, -mail: firstname.lastname@example.com
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1. INTRODUCTIONThe contemporary Information Society permits to introduce the 3D modelingpossibilities in the www environment. By joining the principles of e-learning, 3Dmodelling and communication medium of the global network is possible to realize aDistributed Learning Environment (DLE) that offers a set of knowledge in the area of 3Dsimulation and virtual reality. During the last years 3D virtual environments represent anew form of learning environment that can involve synchronous and asynchronouslearning opportunities that provide a three-dimensional simulated learning situation,rather than replicating a traditional classroom, laboratory and university [1, 2]. Inaddition, the combination of X3D modeling language and Adaptive HypermediaArchitecture (AHA) is possible to represent 3D educational virtual environments [3]. Allthese prerequisites give a real possibility to make a DLE in the field of 3D simulationand virtual reality.The paper presents a possibility for DLE architecture investigation by using thedeterministic apparatus of Petri Nets [4, 5]. The main goal is to build a model as anasynchronous Petri net (PN) completed by previously defined basic model primitives formain participants in the processes of information servicing [6]. In this reason averification of these primitives is made and a global model is presented. The modelexecution is made and some analytical results are discussed.2. DLE ABSTRACT DESCRIBING AND BASIC MODELS VERIFICATIONA basic general abstract model of the learning interactions in the DLE is designedand is shown in fig. 1. The connection path between a user and an information resourceis marked and basic participants in the distributed learning process are defined:9 Set of users U = {U i / i = 1 N } , U;9 Set of learning resources R = {R j / j = 1 M } , R;9 Set of transmitters T = {Tq / q = 1 K } , T;9 Distributor ( D ) that routs all information objects in the communication medium.The basic models (primitives) describing these DLE components by using PN arepresented in [6]. Two different interactions between nodes are defined:TqR j (for9 requesting a learning resource initialized by user req : U i
U i U ; R j R );
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e-Learning099 responding by sending the requested learning object (information block)TqInf : R j U i (for U i U ; R j R ).Users
0 = (2,0,0,0)
0 e[ j ].D at 0 :
1, + 1, 0, 0 + 1, 0, 1, 0
0, 0, + 1, 1The model primitive execution is the following:
ResourceModified model primitive (t is an introduced passive transaction) for the verificationpurpose is shown in fig. 3.D- pA pB pC pDD+ pA pB pC pD
R (j)
0 = (1,1,0,0) tR;
0 = (1,1,0,0)
1, 1, + 2, 0
+ 1, 0, 0, 1 tt9 Model execution (PN evolution): 0 = (1,1,0,0)
(0,1,0,1) t 0 .S
TransmitterModified model primitive is shown in fig.4 and two additional passive positions (x,y) are added.DINxDOUT
In_RT (q)
1, + 1, 1 , + 1
+ 1, 1, + 1, 1
9 Incident matrix: D = D D = +
P = {DIN , DOUT ,{ pT 1 j , pT 2 j / j = 1 K }}I (tT 1 j ) = {DIN , pT 1 j }O(tT 1 j ) = {In _ R j , pT 2 j }I (tT 2 j ) = {Out _ R j , pT 2 j }O(tT 2 j ) = {DOUT , pT 1 j }
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T = {tT 1 j , tT 2 j / j = 1 K }
p7Fig.6. PN-model of the information servicing for a DLE-segment initial marking 0= (2,0,0,0,0,1,0,0,1,0,0)The matrix definition of the segment from fig. 6 is the following:
2 0 0 0 0 0 0 0 0 0 00 0 1 0 0 0 0 0 0 0 0
1 1 0 0 0 0 0 0 0 0 01 0 0 0 0 0 0 0 0 0 0
0 1 0 0 0 0 0 0 0 0 00 0 0 0 1 0 0 0 0 0 0
0 0 0 1 0 0 0 0 0 0 00 0 1 0 0 0 0 0 0 0 0
0 0 0 1 0 1 0 0 0 0 0
0 0 0 0 0 0 1 1 0 0
0 0 0 0 0 0 1 0 0 0 10 0 0 0 0 0 0 1 1 0 0
0 0 0 0 1 1 0 0 0 0 00 0 0 0 0 0 0 0 0 1 0
0 0 0 0 0 0 0 0 0 1 0
0 0 0 0 0 0 0 0 1 0 1
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e-Learning099 Calculating of the incident matrix:
1, + 1, 0, 0, 0, 0, 0, 0, 0, 0, 0 + 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0
0, 1, 0, + 1, 0, 0, 0, 0, 0, 0, 0
0, 0, + 1, 0, 1, 0, 0, 0, 0, 0, 0 +
0, 0, 0, 0, + 1, + 1, 1, 0, 0, 0, 1 0, 0, 0, 0, 0, 0, 0, 1, 1, + 1, 0
0, 0, 0, 0, 0, 0, 0, 0, + 1, 1, + 1 9 Next marking defining for the presented 0 the next marking will be defined asfollows:
The model evolution for a single time execution is presented in Table 1. Fig. 7shows the general scheme of the model evolution (the tree of the reachability).Table 1.Places and markst
e-Learning09The model investigation is realized by the analysis of the tree of the reachabilityand of the basic model conditions. The main results are generalized below.9 Reachability the model allows cyclic execution of the basic phase oftransactions fairing.9 Liveliness the model is alive because the evolution permits at each step to fire(activate) at least one transaction (see Table 1).9 Blocking no situation during the evolution can block the model execution at theone-user access to the learning resource.9 Boundness the model is 2-bounded because (pi)2 (see Table 1).9 Safety it may be accepted that the model is safe because for all transactionsthe number of input arcs is equal to the number of output arcs.9 Persistence the total number of marks in the model is the same during eachexecution step.9 Conservativeness yes, based on the same reason.5. CONCLUSION AND FUTURE WORKThe discrete modelling and investigation presented in the paper will help to definethe most important points of the DLE architecture and the main problems connected tothe organization of processes for information servicing. As a next phase in our work weplan to design suitable DLE architectural model and to realize 3D prototypes as adistributed information objects in DLE.ACKNOWLEDGMENTThe research presented in this publication is funded by the Internal ResearchProject 091001-09, Research and Development Sector at Technical University ofSofia, 2009.REFERENCES[1] Bischoff, A. Bandwidth saving synchronous collaboration environment for virtualuniversities. Proceedings of Virtual University, Bratislava, Slovakia, Dec. 2004, pp. 4448.[2] Bouras, C., T. Tsiatsos. Distributed virtual reality: building a multi-user layer forthe EVE Platrorm. Journal of Network and Computer Applications, 27, 2004, pp.9111.[3] Chittaro, L., R. Ranon. Adaptive Hypermedia Techniques for 3D EducationalVirtual Environments. IEEE Computer Society, July/August, 2007, pp.31-36.[4] Hein, James L., Discrete Mathematics (2nd ed.), Jones and Bartlett Publishers,Sudbury, Massachusetts, ISBN 0-763-72210-3, 2003 (731 p.).[5] Girault, Caude, Rudiger Valk. Petri Nets for Systems Engineering (A Guide toModeling, Verification, and Applications), ISBN 3-54041-217-4, Springer-Verlag, Berlin,Heidelberg, 2003 (608 p.).[6] Romansky, R., E. Parvanova. Organization of Distributed Learning EnvironmentModeling by using Petri Nets. Proc. of the 5th Intl Conf. on e-Learning and theKnowledge Society, Berlin, Germany, 31.08-01.09.2009.ABOUT THE AUTHORSAssoc. Prof. Radi Romansky, PhD, Department of Computer Systems, TechnicalUniversity - Sofia, Phone: +359 2 965 32 95, e-mail: email@example.com Parvanova, PhD Student in Computer Systems Dept., Technical University Sofia, e-mail: firstname.lastname@example.com
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INTRODUCTIONIt is commonly agreed that emotions have a strong impact on our behaviour.Students with well developed abilities and trained skills show the expected behaviour ifthey get motivated by internal or external emotional triggers. Strong negative emotionsas fear and anxiety can block the learning behaviour. Happiness has a positive effect onthe learning behaviour. But emotions and their impact on the e-learning behaviour arenot well understood and a lot of research is needed. Some students miss the socialsupport and interaction in e-learning environment. A strong motivation, discipline andtime scheduled learning is needed to survive in a distant learning environment. For thatreason the Open University invested a lot of effort in setting up networks andcommunities of students that are remote in space and time.In the past there was a strong impact on the cognitive aspects in the learningtheories. Emotions were neglected or researched independently from cognition. Onlyrecently researchers studied the impact of cognitive and emotional aspect on learning.Goleman [8] introduced the concept of emotional intelligence similar to the classicalcognitive intelligence. Researchers as Darwin [4], Damasio [5], Averill [1], LeDoux [11]contributed a lot to the understanding of emotions and their impact on learning.In a face to face learning environment emotions can be assessed from thenonverbal behaviour of students. In distant learning without or with digital teacher theassessment of emotions is more complex. A common way is to use questionnaires. Butquestionnaires can not be used for continuous real time assessment of emotions. Thatis the reason we developed a surveillance system using cameras and microphones torecord nonverbal behaviour of students and analysed those recordings with ourEmoRec system.After Darwin, Ekman [7] spent a lot of research on the automatic recognition offacial expressions. He claimed that 6 emotions are universal namely happiness,sadness, disgust, anger, fear and surprise. Many systems have been developed torecognise the six basic emotions in speech and recordings of facial expressions. But indaily life most emotions are not pure but blended and of varying intensity. And even thesix basic emotions can be displayed in many ways. This can also be expected in an elearning environment.Most emotion recognition systems are based on classifiers. To train a classifier ahuge database of emotional expressions is needed. Recording and annotating adatabase of emotional expressions is very time consuming. Most available databasesare acted emotions by trained people and not recordings of spontaneous emotions. Incase of recognition of facial expressions unsolved problems are varying lighting- 77 -
e-Learning09conditions, changing posture and occlusion. In case of speech, multiple speakers,background noise and reverberation are difficult to handle problems.The outline of this paper is a follows. In the next section we will report aboutrelated work. In section three we will introduce the technologies used to recognizeemotions in sound and video recordings. Next we report about our experiments and wewill end with a section conclusion and future work.RELATED WORKIn [14] ORegan reports studies of online learning in which emotion plays animportant role. The Web offers the perfect technology and environment for personalizedlearning. Martinez [12] studied individual learning on the Web with a focus on emotions.At eduweb [http://www.eduweb.com], the goal of the designers is to develop the mostengaging and effective online learning experiences possible. To this end, we engage inresearch to better understand learning theory, learner preferences and engagement,and educational outcomes. Schaller and his colleagues report about the negativeemotions students experience when they have to navigate the first time through learningsites. In [19] Wegerif centres on the sense of isolation that online study may engenderamong learners, a factor often ignored by many educators, but one that ignored bymany educators, but one that may make the difference between a successful and anunsuccessful online learning environment for many students. The importance of aproper appreciation of the learners social context is stressed, as is the concept of thevirtual self that individual learners may choose to portray during online communication.Ng [13] reported about online learners showing fear during electronic communication.Students educated with Twitter, Weblogs and Facebook probably require social andcommunicating abilities to handle his negative emotions. Hara and Kling [9] studied thefrustration on line learners experience with badly designed or non-functioning onlinelearning environments. In [15] Rothkrantz introduced e-learning in virtual environments.A virtual University was designed in Second Life. The focus was on the design ofemotion in the social interactions of students represented as virtual characters(Avatars).FACIAL EXPRESSION RECOGNITIONIn the case of video data processing, we have developed an automatic system forthe recognition of facial expressions for both still pictures and video sequences. Therecognition was done by using Viola&Jones features and boosting techniques for facedetection [18], Active Appearance Model AAM for the extraction of face shape andSupport Vector Machines SVM for the classification of feature patterns in one of theprototypic facial expressions. For training and testing the systems we have used CohnKanade database [10] by creating a subset of relevant data for each facial expression.The Active Appearance Model AAM [3] makes sure the shapes of the face and ofthe facial features are correctly extracted from each detected face. Starting with thesamples we have collected from the Cohn- Kanade database, we have determined theaverage face shape and texture (Figure 1).
Fig.1. The mean face shape (left) and the mean face texture aligned to the mean shape(right).
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e-Learning09professional actors (five female and five male). The result consists of ten utterances(five short and five long sentences). The length of the utterance samples ranges from1.2255 seconds to 8.9782 seconds. The recording sample rate is 16kHz. The finalspeech data set contains the utterances for which the associated emotional class wasrecognized by at least 80% of the listeners. Following a speech sample selection, aninitial data set was generated comprising 456 samples and six basic emotions (anger:127 samples, boredom: 81 samples, disgust: 46 samples, anxiety/fear: 69 samples,happiness: 71 samples and sadness: 62 samples). The Praat [2] tool was used forextracting the features from each sample from all generated data sets. According toeach data set frame configuration, the parameters mean, standard deviation, minimumand maximum of the following acoustic features were computed: Fundamentalfrequency (pitch), Intensity, F1, F2, F3, F4 and Bandwidth. All these parameters formthe input for separate GentleBoost classifiers according to data sets with distinctsegmentation characteristics. The GentleBoost strong classifier is trained for amaximum number of 200 stages. Separate data sets containing male, female and bothmale and female utterances are considered for training and testing the classifiermodels.In Figure 4 we display the architecture of a system that is able to recognizeemotions given a speech input in a real time setting.
e-Learning09about the system, administrators and lecturers. All students started interaction to see ifother students suffer from the same problems and were looking for social support.Experiment 3Design: In the third experiment students in subgroups of three students have toplay a game with a race car simulator (TORC). There were three kinds of tracks: onestraight line, a curvy track and a track with a lot of billboards with funny pictures orpictures of traffic accidents. The car drivers have to pass the tracks as soon as possible,Car crashes, violating the traffic rules results in penalties. Every member of the grouphas to drive, every player gets a score and the goal was to reach the high score as ateam. The blink rate of the player was assessed by a sensor. The assignment was tofind the correlation between the eye blink rate and the track condition.
e-Learning09REFERENCES[1] Averill, J A connectionists view of emotion. Emotion: Theory, Research andExperience, Vol. 1, Theories of Emotions, Academic Press, New York, pp 305-337, 1980.[2] Boersma, P., Weenink, D., Praat: doing phonetics by computer (version 4.3.14)[Computer Program], 2005.[3] Cootes, T. F., G. J. Edwards, C. J. Taylor, Active appearance models, Lecture Notesin Computer Science, vol. 1407, pp. 48498, 1998.[4] Darwin, C., The expressions of emotions in Man and Animals, The University ofChicago Press, Chicago, 1872/1965.[5] Damasio, A., Descartess Error: Emotion, Reason, and the Human brain, Papermac,London, 1996.[6] Datcu, D., L. J. M. Rothkrantz, "The recognition of emotions from speech usingGentleBoost Classifier", CompSysTech'06, June 2006.[7] Ekman, P., The nature of emotion: Fundamental questions. Oxford University Press, pp7-19, 1994.[8] Goleman, D., Emotional Intelligence, Bantam Books, New York, 1995.[9] Hara,N. , R. Kling, Students distress with a Web-based distance education course: Anethnographic study of participants experiences, Center for Social Informatics, IndianaUniversity, 2000.[10] Kanade, T., J. F. Cohn, Y. Tian, Comprehensive database for facial expressionanalysis, Proc. of the 4th IEEE Int. Con. On Automatic Face and Gestures Recognition., France,2000.[11] LeDoux, J., The emotional Brain: The mysterious Underpinnings of Emotional Life,Phoenix, London, 1999.[12] Martinez,M., Key design considerations for personalized learning on the Web,Educational Technology and Society, Vol. 4, No.1 pp 26-40, 2001.[13] Ng, K-C., Using e-mail to foster collaboration in distance education. Open learning,Vol. 16, No. 2, pp 191-200, 2001.[14] ORegan, K. Emotion And E-Learning. JALN Volume 7, Issue 3, 2003.[15] Rothkrantz, L.J.M. E-learning in virtual environments, Communication & Cognition,Vol 42, No. 1&2, pp 37-52, 2009.[16] Vapnik, V.,The Nature of Statistical Learning Theory, Springer- Verlag, New York,1995.[17] Vapnik, V., Statistical Learning Theory, John Wiley and Sons, Inc., New York, 1998.[18] Viola, P., M. Jones, Robust Real-time Object Detection. Second InternationalWorkshop on Statistical and Computational Theories of Vision-Modeling, Learning, Computing,and Sampling, 2001.[19] Wegerif, R., The social dimension of asynchronous learning networks. Journal ofAsynchronous Learning Networks, Vol. 2, No. 1, 1998.
INTRODUCTIONVisualization is a technique for analysis of multidimensional data aimed forheuristic analysis of similarity/dissimilarity structure of the objects represented inmultidimensional space by means of mapping of multidimensional points to 2dimensional plane. Such a mapping can be implemented using methods ofmultidimensional scaling (MDS) aiming to preserve the structure of distances. Theproblem of MDS was formulated in [3] as a problem of minimization of STRESSfunction. The term multidimensional scaling was accepted by statisticians and users ofthis technique in social sciences ([6], [1]). The paper by Sammon [8] originated thedevelopment of a special version of MDS. The subsequent papers on implementationand application of Sammons method use the term nonlinear mapping technique; seee.g. [2]. Applications of Sammons method are normally oriented to the problems ofpattern recognition. Although different aspects of MDS have been investigated, crucialimplementation difficulties remain not resolved. In this paper MDS is considered as aspecific problem of global optimization, and as a tool of visualization of information onsearch process in global optimization.FORMULATION OF MDS PROBLEMLet us give a short formulation of the problem. The matrix ij , i, j = 1,..., n gives thepair wise dissimilarities between n objects, and it is symmetric. Dissimilarities are datafor MDS; for example, they can be obtained experimentally. In case the original data isa set of points in multidimensional space Rd, the dissimilarities are defined as distancesin this space. The points xi R m , i = 1,..., n, should be found whose inter-point Euclideandistances fit the given dissimilarities. The embedding Euclidean space normally is 2dimensional (m=2), but other dimensionalities may be also interesting for someapplications, (m<d). To find the points xi the STRESS function f (X) should beminimized,
i< j
e-Learning09LOCAL MINIMIZATIONIt is well known [4] that f(X) is differentiable at a local minimum point, i.e. if X is alocal minimizer, then the following equalities and inequalities are valid kif ( X )= 2 wki (1 )( x kh x ih ) = 0, d ij ( X ) > 0, i, j = 1,..., n.x khd ki ( X )ikIn fact, the more general result may be proven:Proposition. Let L(t), -<t<, be a line in R N , containing a point at which f() isdifferentiable. Then f() is differentiable at any point L(t*) R N , where t* is the localminimizer of (t)=f(L(t)).It follows from the proposition that a local descent trajectory escapes the points ofnondifferentiability of f(). Therefore, a fast local descent method may be applied tofind a local minimizer of f(). It is well known that variable metric methods are efficientfor local minimization of f(X) in case of not too high dimensionality N. For very highdimensionalities a conjugate gradient method seems promising. From a theoretical pointof view the rate of convergence is most important feature of an algorithm. The superlinear convergence rate of a conjugate gradient method may be proved under mildassumptions on an objective function. However, the quadratic convergence is provedassuming the norm of Hessian of the objective function be bounded from zero. Let usanalyze the second directional derivative of f(X) at a local minimum point X * withrespect to the direction S: (t , S ) = f ( X * + t S ), ' (0, S ) = 0,
m ( x ih x jh )(s ih s jh ),d ij ( X ) h =1
d (X )
(d ij2 ( S ) d ij2 ( X ) [
(xh =1
x jh )(s ih s jh ) ] 2 ) }.
At a local minimum point the inequality ' ' (0, S ) 0 holds for any S. For thedirections S corresponding to the translations and rotations of the embedding space theinequality is reduced to the equality ' ' (0, S ) = 0 . The latter equality implies thedegeneracy of Hessian. To ensure the quadratic convergence of the conjugate gradientmethod the problem should be regularized. The simplest regularization is the fixation ofseveral variables to exclude invariance with respect to translations and rotations of theembedding space; see e.g. [9]. However, specific positions of few points have noadvantages for heuristic analysis of the images in the embedding space. Theregularization can be achieved excluding invariance with respect to translations androtations (in the 2dimensional case, m=2) by the equality constraints:nnn(2)i =1 xi1 =i =1 xi 2 =i=1 xi1 xi 2 = 0.Proposition. The Polak Ribiere conjugate gradient method converges to a localminimizer of regularized minimization problem quadratically with respect to the numberof iterations including 2n-3 exact line searches.Since the analytic expressions of the first and second directional derivatives areavailable, then a high precision line search method based on forth degree polynomialinterpolation may be easily implemented.A HYBRID METHODSeveral algorithms of minimization of the STRESS are available. The theoreticalresults of the previous section show that the nondifferentiability of (1) is not a concernif a local descent method is used. Therefore, a conjugate gradient method with- 84 -
e-Learning09regularization of minimization problem is a strong competitor for other well knownmethods. The latter method may successfully cope with high dimensionality. Therefore,the real difficulty is caused by the multimodality of the STRESS.The majorization method, which is especially tailored for MDS, may escape somelocal minima. However, like the other local methods, it provides a solution essentiallydepending on a starting point [1]. A local descent may be extended for multimodalproblems using the tunneling approach. The possibilities of such an extension for MDSare discussed in [1]. The combination of majorization method with genetic type choice ofstarting points was proposed in [5]. In the latter the genetic algorithm based method ofMathar and ilinskas [7] was modified substituting a variable metric local descent withmajorization method. The results of limited experimental testing showed that a genetictype approach can be suitable for global minimization of (1). In the references onnonlinear mapping various versions of descent method are of primary interest.However, combinations of descent with general global search methods, e.g. simulatedannealing, are claimed promising in [2].The proposed combinations of a local descent method and global search normallyconsider the general global search schemas. The idea of this paper is to tailor a specificversion of a genetic type global search based on assumptions on the features of theSTRESS.The majority of the authors consider the unconstrained minimization of (1). In sucha case the same minimum value may be obtained at different points on the orbit of localminimizers corresponding to invariance of f(X) with respect to translation and rotation ofthe embedding space. Therefore several copies of the same local minimum may beobtained, but with very different minimizers. It is difficult to handle such information onlocal minima to perform rational search for a global minimizer. In our conjugate gradientalgorithm the ortogonalization (2) prevents multiplication of the minimizers. The analysiscarried out for several sets of data shows that different local minimizers are locatedrather close each to other. Geometrically this feature of the STRESS may be explainedas a small structural difference between the graphs in the 2dimensional embeddingspace corresponding to different local minimizers of f(X). The geometric interpretationimplies a hypothesis that graphs corresponding to local minimizers are composed ofsimilar semi-optimal sub-graphs.Different local minima are considered as the ideal representatives of differentbreeds. They are used for crossover. The initial population of the size p is generated bymeans of local descent method from the random initial points. The parents are chosenat random with uniform distribution. The structure of chromosomes is not known. Thegraph in the embedding space is considered the phenotype. The hypothesis is acceptedthat crossover of chromosomes imply the crossover of characteristics of phenotype. Thelatter is modeled as the crossover of the graphs in the embedding space, i.e. somepoints xi R 2 of one parent graph and remaining points of the other parent graph aretaken to compose the descendant graph. The break position is generated randomly withuniform distribution in the interval [1, n]. The larger number of points is taken from thefittest parent. The mutation is modeled as the random summands to the components ofgraph coordinates; the distribution of the random variable is uniform in the interval [-r, r].Two selection mechanisms have been investigated: 1) the descendants survive and theparents die; 2) each descendant competes with a randomly chosen individual of currentpopulation. The number of generations modeled to find the global minimum is denotedg.EXPERIMENTAL TESTINGTo assess the efficiency of the regularized conjugate gradient method (CG) forlocal minimization of (1) CG was compared with the known method based on- 85 -
e-Learning09majorization approach (MA) [5]. The convergence of MA to local minimum point isproved, e.g. in [6]. Moreover, it may be expected that bad local minima will be avoided.Both methods (CG and MA) were implemented in MATLAB.Two known sets of data were used. The first set, presented in Table 1 a), contains ij for ten soft drinks whose dissimilarities are obtained by means of experimentaltesting [1]. The second set of data corresponding to the proximities of 13 facialexpressions [1] is presented in Table 1 b).Table 1. Data for visualization.a)1.27 1.69 2.04 3.09 3.20 2.86 3.17 3.21 2.381.43 2.35 3.18 3.22 2.56 3.18 3.18 2.312.43 3.26 3.27 2.58 3.18 3.18 2.422.85 2.88 2.59 3.12 3.17 1.94b)1.55 3.12 1.31 1.70 2.850.4053.06 1.64 1.36 2.810.825 0.2543.00 2.95 2.560.557 0.269 0.2111.32 2.910.115 0.267 0.898 0.3782.970.297 0.388 0.927 0.605 0.2340.434 0.853 1.187 0.978 0.712 0.1360.490 0.131 0.256 0.421 0.590 0.518 0.8470.625 0.188 0.074 0.045 0.477 0.545 1.020 0.2630.155 0.484 0.925 0.492 0.222 0.417 0.544 0.545 0.7100.168 0.581 0.792 0.542 0.434 0.472 0.431 0.379 0.658 0.1980.657 0.743 0.830 0.893 0.816 0.466 0.157 0.649 0.977 0.493 0.4830.393 0.451 0.847 0.348 0.160 0.489 0.918 0.605 0.655 0.412 0.351 1.265To compare the performance of both methods 100 runs have been performed withboth sets of test data. The starting points were generated randomly with uniformdistribution in the cube [1.2,1.2] N . Both methods used the same random starting pointand the same stopping condition: || f ( X k ) ||= < 0.001 . The averaged values of resultsare presented in Table 2.Table 2. The comparison of the performance of CG and MA algorithmsCG, Test 1MA, Test 1CG, Test 2MA, Test 2
Time (s)2523
The known best value of the STRESS with the first set of data is 11.746, andregion of attraction of the corresponding minimizer makes 4% of the feasible region. Bymeans of CG a minimizer with the value better than 11.75 has been found five times,and by means of MA has been found once. 75 times the value found by means of CGwas better than the value found by means of MA.The second set of data defines the STRESS function, which is very likelyunimodal. All 100 runs for both methods stopped in the vicinity of the same localminimizer. Average time of minimization of this rather simple function by CG is againconsiderably better than by MA. In all runs a found minimum was slightly better for CGthan for MA.- 86 -
e-Learning09Time of local descent to a local minimizer depends on dimensionality of a problemand on the stopping condition. The dimensionality of minimization problemscorresponding to Test 1 and Test 2 is equal to 20 and 26 correspondingly. The third testproblem is 2-dimensional representation of vertices of the 5-dimensional cube. Thevertices are numbered according their digital representation. Since the number ofvertices is equal to 32, then the dimensionality of the minimization problem is equal to64. The local descent for the problems of such a dimensionality is time consuming. Inthe Table 3 the results of two runs are presented. The results show that the time of localdescent for 64-dimensional problem is considerably larger than the time for the 20dimensional cases. The solution with the prescribed tolerance of gradient norm bymeans of MA was not found.Table 3. Dependence of computing time on the stopping condition
Time (s)Iterations =0.00135732281000
Since the CG has been shown faster than MA, the former was used to construct aglobal search algorithm. The size of population, number of generations, crossovermethod, and mutations intensity has to be chosen experimentally. The experimentswere performed with the Test 1. Summarizing the results, the following parameters maybe recommended: p=10, g=3, competition selection mechanism, r=0.3. The averagedresults of 100 runs for Test 1 are the following: the STRESS value equal to 11.77,number of line search iterations equal to 812, minimization time equal to 351. Minimalfunction value with accuracy no less than 1% was found 95 times. The average resultsobtained by means of evolutionary algorithm are considerably better than thoseobtained by means of local descent method from the random initial points.The evolutionary algorithm was applied also to Test 3. The best-found value of theSTRESS function was 141.11, number of line search iterations was 1470, and solutiontime was 7044. The 2dimensional image of vertices, corresponding to the globalminimizer, is presented in Fig.1 (left-hand side). In Fig.1 (right-hand side) the imagecorresponding to a local minimizer is presented. Although the difference of the STRESSvalues is insignificant, there are clearly visible differences of the images.
e-Learning09trajectories of local search from seven random initial points (squares) are presented.The function (1) with the data of Test 1 was minimized. The picture supports thehypothesis that different local minimizers of (1) are close each to other.
INTRODUCTIONThe advent of the World Wide Web has introduced new possibilities for providingeducation to large amounts of students. E-learning presents significant advantagescompared to classical education in that it can provide educational services to studentswithout posing spatiotemporal restrictions. The above have led to the increasedpopularity levels of this type of education and have contributed to its adoption by manyeducational institutions worldwide. However, due to the fact that e-learning lacks faceto-face communication, it has been observed that learners do not receive the sameamounts of feedback regarding the quality of their work, as they would typically receivein traditional education courses.A solution to this problem can be achieved through the use of peer assessment.According to this methodology, students evaluate each others work, acting as bothreviewers and authors. Peer assessment has been found to benefit the educationalprocedure since students receive more feedback and gain deeper understanding of thecourse material [1 - 3]. Instructors also benefit from this methodology, since the qualityof the projects they need to assess is better, a fact that decreases the workload for thistask and enables them to focus on more constructive aspects of their work.In this paper, an online peer assessment platform and its functionality arepresented. The rest of this paper is organized as follows: The second section describesthe course upon which the peer assessment platform was applied, as well as themethodology steps followed. The next section provides a detailed description of theonline platform. The final section concludes the paper and provides possible futureextensions of this work.PEER ASSESSMENT FOR E-LEARNING COURSESThe developed platform was used in an introductory level e-learning course onWeb Design. The course is provided by the Multimedia Technology Laboratory elearning team of the National Technical University of Athens [4], through the Moodleopen-source LMS platform [5].The course is offered twice a year: at the Spring and Fall semesters. It is ofintroductory level and is targeted towards adults of various educational backgroundsthat range from high-school graduates to master degree holders. It consists of seveneducational sections, during which learners are presented with the educational material.At the end of each educational section, students are required to submit a project, in theform of a web site, using the material taught in this section.Then, students are requested to participate in the peer assessment procedure.Specifically, learners are asked to provide comments on two randomly selected projects- 89 -
e-Learning09of their fellow classmates. Thus, at the end of a peer assessment step each student willhave reviewed two projects and will have received the comments of two reviewers.Students grade each others projects by filling in a form with questions regarding thedesign, technical soundness, functionality and overall impression of the project underevaluation. Each question is answered through a five item Likert scale, ranging from 1(negative impression) to 5 (positive impression). To evaluate if the reviewer has helpedthe author to enhance the quality of his work, each student is asked to rate thereviewers comments, regarding his perceived degree of usefulness. It should be notedthat students are considered potential reviewers for the peer assessment procedure of asection, only if they have submitted a project for this particular section. Finally, toprevent vandalism, the peer assessment procedure is validated by the instructors whocan read and, if necessary, edit student comments prior to sending them the authors.PLATFORM DESCRIPTIONThe developed platform has been designed to facilitate students and instructors toimplement the peer assessment procedure. The platform has been developed throughthe PHP and AJAX technologies.To this end, the platform can support different Learning Management Systems andautomatically extract student data regarding submitted projects and possible reviewers.The platform uses a database, separate from the one used by the LMS, to store thenecessary data. This local data storing feature helps in reducing the response times ofthe platform and ensures its independency from the LMS used in a specific course.The developed platform can be used by both the instructors and the students. Thefirst have access to a number of tools that help them organize and administer the peerassessment procedure of a class while the second view a more simplified aspect of theplatform.Instructors ViewThe teacher has the following options: Course Overview: Using this feature, the instructor can see the coursedescription, which includes general information regarding the course goalsand material. Insert and edit questions: Through this option, the teacher is able to editthe review form that the reviewers will be asked to fill in, upon evaluatingthe project of their fellow students. Peer assessment overview: This option is one of the most important andmost frequently used. Through this platform function, the trainer is able toview the number of submitted projects per section, the possible reviewersper project, as well as the number of projects that have not yet beencommented by a reviewer (Figure 1). Section overview: Through this functionality the instructor can select andmanage a specific course section. The actions that can be taken in thislevel are: assigning reviewers to authors, viewing and validating newuploaded comments. Student overview: According to this function, the teacher can see the totalnumber of projects that each student has been assigned to review, thenumber of projects that the student has not commented and the totalnumber of comments that this trainee has submitted. This option has beendesigned to be available from every page of the platform, using the AJAXtechnology, enabling the instructor to view the status of the studentswhenever it is necessary (Figure 2).- 90 -
E-mail notifications: Using this functionality, the instructors can send emails to the students to inform them about a new comment or a newproject for review.Platform messages: The platform displays messages to the instructorregarding the number of comments that need to be validated and projectsthat have not yet been matched with a reviewer.
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e-Learning09REFERENCES[1] Sluijsmans, D. M. A., Brand-Gruwel, S., Merrienboer, J. J. G. v., Peerassessment training in teacher education: effects on performance and perceptions,Assessment and Evaluation in Higher Education, vol. 27, pp. 443454, 2002.[2] Topping, K., Peer assessment between students in colleges and universities,Review of Educational Research, vol. 68, pp. 249276, 1998.[3] Wen, L. M. C., Tsai, C.-C., University students perceptions of and attitudestoward (Online) peer assessment, Higher Education, vol. 51, pp. 2744, 2006.[4] Medialab, E-Learning Services, Multimedia Technology Laboratory, NationalTechnological University of Athens, http://elearn.medialab.ntua.gr/, 2009.[5] Moodle, Moodle LMS, http://moodle.org/, 2009.ABOUT THE AUTHORSDr. Ioanna Lykourentzou, PhD, School of Electrical and Computer Engineering,National Technical University of Athens, Greece, Phone: +30 210 772 2538,-mail: email@example.com Giannoukos, School of Electrical and Computer Engineering, NationalTechnical University of Athens, Greece, Phone: +30 210 772 2538, -mail:email@example.com. Vladimir Lazarov, PhD, Institute of Parallel Programming, Bulgarian Academyof Sciences, Bulgaria, Phone: +359 2 870 273, E-mail: email@example.com. Vassili Loumos, PhD, School of Electrical and Computer Engineering,National Technical University of Athens, Greece, Phone: +30 210 772 2537,-mail: email@example.com.
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INTRODUCTIONHigher education institutions throughout the country are legally obliged to offerdistance learning as equal to the other forms of education. In the Web-basedenvironments developed for distance learning, the basic method for identification ofusers is to register them. This causes some inconvenience for participants in thelearning process and imposes requirements to them. Some aspects of the registrationprocess in a system for "Provision of information on survival in times of crisis by HandHeld devices" are discussed further below.LAYOUTUser registration in the applications for distance learning is required to identify theusers. The provided username and password enable the user to use the educationalresources from different workstations and personalize the learning process. Theregistration at this stage is considered a necessity, which imposes certain aspects in theactivities of the students. It has the following characteristics:1. Users understand the need for registration but evaluate it as an annoyingrequirement of the site2. The users are especially critical of the following fields of the registration form:2.1. Fields which have no relation to the purpose of the site and the interests of theuser, so obviously the data is needed for studies of the target groups.2.2. Fields which require filling of data about other people related to the user.2.3. Fields which require data that violates the confidential use of the site resourcesor data that reveals the physical identity of the user.2.4. Fields of the following type: text field or text area, which require text inputfrom the keyboard.2.5. Fields that require a creative description or a position on a certain topic,especially if it takes longer to consider the answer.2.6. Fields which require the information to be filled in a foreign language, especiallywhen the data is of descriptive nature.2.7. Fields which require completion of difficult to understand anti-spam code.2.8. Fields which require a high degree of uniqueness of the data.2.9. Fields with a high degree of data validity verification by the server.3. Users understand the need for confirmation of registration via E-Mail, but theycomplete it with some nervousness and reluctance.4. The users are not sure if their personal data is stored under the Law on personaldata, so they often fill in false one.5. The users are fairly critical to some administrators' attempts to verify the accuracy orvalidity of the submitted data in some special ways.6. Having registered, users do not accept to be treated as beginner users with limitedrights to use the web site resources.- 94 -
e-Learning09The registration process is carried out by the site administrator. How effectively forthe system the registration will be done depends on administrator's knowledge andskills, and what initial impression of the site will be received by the user who has passedwith approval its "Welcome zone". Some of the most common mistakes of the siteadministrators are listed here below:1. It is considered true that if a user is registered on the site, he is motivated to visit itregularly in the future.2. The registration process discourages users with lower motivation from using the siteresources.3. Registration is an opportunity to collect statistical information about the target groupsof visitors. It is also a way to identify the scope of site usage.4. Registration on the web site "estranges" the user from visiting other sites with similarcontents.5. Registration is considered a process of identification with the group affiliation andhierarchical position in the social communities.6. Registration is accepted as an act of goodwill and commitment to the rules of siteusage.7. Registration, in particular, the publication of personal data, is an act of user's trust inthe site. Accordingly, the web site administrator is to establish further contacts withthat user.8. Registration on the web site is an opportunity to announce, or popularize the use ofother services offered on the site, or even those of third parties.9. The following statement is considered true that any future user will carefully read therules of site usage and will conscientiously observe them.10. The administrator, as a managing authority, has the right to impose sanctionsagainst unfair users. Good policy, in respect of unfair users, is to hamper theimplementation of their mercenary plans.11. Administrators often cannot strike the balance between data security, user'sconvenience and quantity of acquired information, and verification of the informationsubmitted in the registration forms.Registration of new users can be classified in three groups, as follows:1. Single and complete - all necessary data is required at the beginning.2. In depth - necessary data is gradually required at each stage of training.3. Combined typeIn order to implement effective distance learning, some data about the users isrequired. A range of data types obtained empirically through the use of a system for"Provision of information on survival in times of crisis by Hand Held devices" is listedhere below and at the following address: http://hand.danysto.info. On concept level,they are consistent with the standards IEEE PAPI and IMS LIP.1889 users have been registered for the period of existence 2006 - 2009. Time forregistration of each user has been surveyed. The results are presented in figure 1.The selected type of registration is single and complete. The user fills ininformation in an entry form with fields classified in the following way:
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e-Learning09From user's point of view, there are help instructions for all of the registration formcontrols. There is a colour indication for data validity at the fill-in stage. The clientchecks the validity of all data. The server verifies all data submitted.Table 1. A set of data types and a "roadmap" of the registration#
Stage ofcertification
User name
E-mail address
Postal address
Monthly income
Place of birth
Chat agentInterests (including educational ones)Hobby
IR - Initial registrationST - Stage of trainingOS - Organizational stageIST - Initial stage of trainingBST - Basic stage of trainingFST - Final stage of trainingSC - Stage of certification
Value [s]65090624103242521
e-Learning09Table 3. Parameters of the statistical interval time for profile editing.Parameters of the statistical intervalTop limit of the intervalBottom limit of the intervalHighest valueLowest valueMiddle valueWidth of the interval
Value [s]360034523123322
Data related to the frequency of user profile update, the number of inactive usersand the number of those who regularly use the resources on the site is highly important.The password is an essential part of the registration process. It provides dataprotection to the user, prevents from identity theft" and is directly related to the jobsecurity of the web site.It is a matter of particular importance to comply with certain rules, some of whichare described below:Good practices for passwords:The size of the password is chosen by the administrator and is usually 4-10characters long. In a secure password a symbol is not used more than once. It isrecommended that all symbols of the password be different - capital and small letters,numbers. The keyboard consists of 256 symbols, and 200 of them can be used in thepassword.The length of the password is of great importance. Commonly, the system requiresa minimum of 4 symbols. The secure password is not shorter than 8 symbols. Ifsomeone obtains an encoded file with passwords, the easiest passwords to be decodedare the shortest ones.When choosing a password, provided that you choose a letter symbol on the righthand side of the keyboard, select a capital letter. In the future, if you enter yourpassword while being watched by your colleague, you can press the Shift keyimperceptibly with the little finger of your left hand.Well-known names are not appropriate for passwords. Otherwise, the passwordcan easily be guessed.It is not recommended to change the password the day before the weekend orbefore going on a leave.Never use the same password for all applications.Besides the types of data presented in Table 1, other types are also needed forthe successful management and development of the web site. They mainly concern thebehaviour of the web site users and reflect their motivation to use the publishedapplications. Some aspects of their use and collection are described below:A good solution is to install and maintain a forum on issues corresponding to thepurpose of the site. A forum appears to be the best place on the Net to identify theneeds and problems of the target group of users. Likewise, groups of interests, socialnetworks and others have been established and maintained.To create conditions for confidential use of the resources on the web site but thebehaviour of the users is to be closely studied, and namely: through whichadvertisement channel they entered the site; through which port they accessed the site;which applications of the so called Welcome zone they were interested in; how long ittook them to familiarize with certain applications reported; how long it took them toregister; which location on the Net they accessed the web site from and what their- 98 -
e-Learning09destination was after leaving it; what is their speed of learning under the specifiedsyllabus (curriculum); to analyze the chosen user names and passwords.To analyze the hardware and software resources of the end terminals: monitors resolution, number of colours, operating system, type and version of the browser.Use of public sources of information. For example, if motivated by the betteradaptation of the site to users, the latter have filled in their job. This suggests moreopportunities for acquiring information from the public sources for these users.Web site developers are facilitated by specialized environments for analysis ofpublished contents in Web and traffic on the web site, such as: Google analytics, Getclicky and etc.CONCLUSIONS AND FUTURE WORK1. At present, registration of users is a reliable and efficient method for theiridentification and adaptation of the web site to their work.2. It is necessary that administrators of Web-based learning systems continuouslymonitor the parameters of the process of registration of their customers andcontinuously optimize it.3. The statistics published about the system for "Provision of information onsurvival in times of crisis by Hand Held devices" shows that the process of registrationneeds to be optimized, namely: reducing the time for initial registration and increasingthe motivation of consumers to systematically update their users profile.REFERENCES[1] David S. Metcalf, M-Learning: Mobile E-Learning, HRD Press, Inc. ISBN,0874259061[2] Norbert Pachler, Ben Bachmair, Mobile Learning: Structures, Agency,Practices, Springer, ISBN 1441905847ABOUT THE AUTHORSSenior Assistant Jordan Stoyanov, Department of Computer Systems, NMU VasilLevski, Phone: +359 88 7 703 041, -mail: mail@@danysto.info.Assoc. Prof. Georgi Stoyanov Todorov, Head of Information Systems Department,Faculty of Mathematics and Informatics, St. Cyril and St. Methodius University of VelikoTarnovo Phone:+359 62 600 461, E-mail: firstname.lastname@example.com
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INTRODUCTIONWeb 2.0 and e-learning 2.0 [7] concepts require an advance developmentapproach and have placed significant pressures on higher education to become moreproductive. The Virtual University would also not remain untouched. Now we shouldthink for implementation of Virtual University 2.0 concept rather than sticking with VirtualUniversity 1.0 model. Todays knowledge is being delivered outside the VirtualUniversity and access to that knowledge is becoming free. A new generation of virtualuniversities with innovative structure to compete are needed nowadays. But in whichdirection virtual universities need to change and how can this be done? What will be therole of Virtual University 2.0 (VU 2.0) in a Web 2.0 world?CHALLENGES BEFORE VIRTUAL UNIVERSITY 2.0Some of the top jobs that will be in demand in the future did not exist today. So VU2.0 is challenged to prepare students for jobs that dont exist yet using technologies thathavent been invented yet. VU 2.0 is intended to provide students with complexunderstanding and problem solving skills in different technical subjects to allow them tobe successful citizens in future digital society.Some of the top jobs that will be in demand in the future did not exist today. SoVU 2.0 is challenged to prepare students for jobs that dont exist yet using technologiesthat havent been invented yet. VU 2.0 is intended to provide students with complexunderstanding and problem solving skills in different technical subjects to allow them tobe successful citizens in future digital society.The use of Web 2.0 technologies in VU 2.0 change the paradigm of learning: froma top-down VU 1.0 model focused to the teacher to a networked approach whereteachers should change their roles to become facilitators of the learning process. Thechange in learning paradigms of Virtual University 2.0 is presented on Fig.1.Teacher CenteredVU 1.0 Learning Paradigm
Learning NetworkVU 2.0 Paradigm
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e-Learning09Learning by doing, collaborative and active learning are essential approaches inLearning Network VU 2.0 Paradigm.WHAT IS VIRTUAL UNIVERSITY 2.0?Virtual University 2.0 is a new concept of knowledge management that will affectfuture virtual education. VU 2.0 is a term describing the impact of e-Learning 2.0 tohigher education from a teaching and research perspective. VU 2.0 focuses to:increasing motivation by cooperation + competition, innovation, creativity andentrepreneurship.WHAT'S WRONG WITH THE EXISTING VU MODEL?Most Virtual Universities 1.0 were built and designed to function effectively in asingle geographic location to a specific group of people. As the world moves toward aglobal economy and information can be accessed from anywhere in the world,universities need to think more critically about how they want to teach future students inthe new digital age, and how they can use WEB 2.0 services in their training process.VU 1.0 vs VU 2.0VU 1.0 maintains systems designed to control students access to learningresources by using software solutions like Managed Learning Environment (MLE).VU 2.0 is allowing students to make their own decisions about information access,creating learning content and encouraging the use of WEB 2.0 services.To get sense of VU 2.0 we will start with analytical comparison of VU 1.0 and VU2.0. In Table 1 are given the basic differences between VU 1.0 and VU 2.0.Table 1. VU 1.0 vs. VU 2.0VU 1.0
E-learning platforms
Students as consumers
Students as creators
Personal aggregators
Knowledge management
Community knowledge
Individual work
Expository learning
Discovery learning
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e-Learning09CHARACTERISTICS OF VU 2.09 VU 2.0 is driven by people VU 2.0 is about people creating content andsharing their ideas in order to create further information;9 Users Add Value VU 2.0 allows users to contribute in increasing the value ofexisting learning content by providing new content;9 VU 2.0 delivers a new way of access and interaction with knowledge Students are encouraged to learn continuously and build a leaning network. Atthe moment students are using many different mechanisms for collaborativelearning: Wikipedia, Facebook, Notemesh, Flickr, etc;9 VU 2.0 students shift from consumers to active users participating ascurators and learning content creators;9 VU 2.0 is a perpetual beta VU 2.0 releases new learning tools gettingfeedback from users;9 VU 2.0 data is a competitive advantage Everybody can contributes as a VU2.0 students and work in progress research;9 VU 2.0 provide open access delivery system: VU 2.0 encourages students todeliver content and share with all;9 VU 2.0 services are beyond single devices VU 2.0 usage is deviceindependent;9 Collaborative culture VU 2.0 adopts creative commons where reuse ispermitted and encouraged.In VU 2.0 not only learning models are changed content organization models arealso challenged. VU 2.0 learning content is organized as: dynamic (real-time content,such as e-mails, instant messages, or conference calls); and moderated (social writingplatforms or wikis that capture information where we did well and how we did it).Learners in VU 2.0 will assemble relevant material on their real-time needs, rather thana structured situation where someone else dictates what kind of learning they need.Teachers will not dictate structured learning. They will sort through all kinds of differentlearning resources and guide students in the right direction for their particular learningneed. The training professional will give them the resources and let them chat,collaborate, share, and discover what they need to learn.VU 2.0 is going to teach in a way similar to TV networks, where the control ofcustomers (students) is not possible. There will be no more scheduling courses oragendas. VU 2.0 learning institutions should be customer-oriented, just-in-time andflexible. The VUs that understand this will be the ones that survive the longest.FUTURE STUDENTS GENERATIONSFirst of all we have to answer the question: Do we really have students who canuse VU 2.0 technologies to enhance their learning?Our students have changed radically. Todays student are no longer the peopleour educational system was designed to teach [9].Todays students are not only digital natives they are also digital learners [8].They prefer to meet with their teachers and colleagues one day a week, and then to usevirtual simulations and surf in WEB the rest of the week to complete coursework.Todays teenagers are Google Generation Students they are experts at usingWEB, but they have problems when it comes to understanding the results displayed onscreen. Although todays digital natives are familiar with advanced technology, they stilldont know everything about using the information they find and they will need us toguide them through the masses of information available to them. This means thatteachers still have a key role to play in VU 2.0 learning.- 102 -
e-Learning09To find out what future students generations will look like, a short analysis of theYouTube clip "A Vision of Students Today" will be presented [2]. The Clip is created bystudents in cultural anthropology and faculty staff at the Kansas State University. Itstarts with a student eye view of an empty lecture hall, before presenting the findings ofa survey into the student experience through placards held up by individual students.The clip is created from an online text edited 367 times by 200 students, who aresurveyed. The clip demonstrates how useful WEB 2.0 can be for universities in marketresearch. At the same time, it is an example of how effective WEB 2.0 technologies arein projects involving collaboration. The survey shows that on average respondentsexpected to read eight books, 2,300 web pages, and 1,281 Facebook profiles thatsemester. Students would write 42 pages for class, but they would write another 500pages of emails. Some interesting students responses will be summarized:9 Students buy hundred dollars textbooks that they never open;9 Students bring their laptops to class, but they do not work on class stuff;9 When students graduate they will have probably a job that doesnt existtoday;9 Students complete readings that are not relevant to their life;9 18% of the teachers know students names;9 Future class size will be more than 100 students.Digital learners are significantly busier than we were when we were students.Todays students: get 7 hours of sleep each night; spent 1,5 hours watching TV eachnight; spent 3,5 hours a day online; listen to music 2,5 hours a day; spent 2 hours ontheir cell phones; spent 3 hours in class; 2 hours eating; work 2 hours every day; 3hours studying - > Total 26,5 per day.Other surveys show that: 35% of all teens blog; 54% post photos online; 19% postvideos; 39% share their own artistic creations online; 26% remix content; 27% maintainweb pages; 28% have created their own blogs.VU 2.0 learning depends on an interaction between teachers who are expert in theacademic use of the technologies for learning and students who are definitely expertsenough to use VU 2.0 learning tools.WEB 2.0 LEARNING TOOLS IN VU 2.0Some Virtual universities have started using WEB 2.0 services in their trainingprocess: lecturers are allowing students access to podcasts and videos of theirlectures; others are encouraging students to collaborate through wikis and using RSSfeeds to organise their own work; many are now giving feedback on essays using Skypeor social sites; others conduct seminars on Second Life Virtual Worlds.WEB 2.0 learning tools offer lots of challenges. Sites like Facebook, Wikipedia andMySpace are going to get bigger: 200 million users on MySpace, 53 million onFacebook, 75 million blogs, 5 million articles in Wikipedia, 100 million videos seen onYouTube every day and 2 billion pictures on Flickr. So how will VU 2.0 use them in thefuture? One of the most important advantages of this is that most of WEB 2.0 servicescan be setup or use for free.Social Writing Platforms: As with most WEB 2.0 tools the key concepts here arecollaboration, sharing and forming communities. Social Software, for example, can beused by VU 2.0 for allowing students to join groups, offering them a unique way ofcommunication and getting extremely valuable feedback. VU 2.0 learning content canbe partially generated by users. Students can have facebook groups dedicated tospecific classes with discussions on some topics, what additional resources andliterature can be used and etc. Social Writing Platforms are useful tools for a variety ofuniversity needs. These services offer an alternative platform for peer editing andsupporting asynchronous writing for distributed members.- 103 -
e-Learning09Wikis are very popular today. In VU 2.0 context they can give students an easyway of creating WEB content, without having any skills in WEB programming. Studentscan use wikis for knowledge management and as a knowledge repository. There iscriticism about the use of Wikipedia for academic purposes, because of themisunderstanding of the power of community. In Wikipedia anyone can add or changecontent and therefore the critics think it is not guaranteed to be a perfect source ofobjective information. VU 2.0 students do not need to cite Wikipedia, they can use it forexample to get a quick overview of a topic related to the one they are studying or toquickly determine the scope of a topic and what other sources are important. Oftenacademic texts only talk about a simple aspect of a topic. Wikipedia entries are usuallydeveloped by a community of people with different interests, so a broader treatment anda list of external links will help to gain a broader perspective on a topic.Social bookmarking can be used as a location to store links and support pages forVU 2.0 lessons: building a source of materials for students, adding commentary, makinga shared space for students to edit or add their own recommendations. The ability tocreate multi-authored bookmark pages can be useful for team work in VU 2.0.RSS feeds are very popular now. Students sign up to something interesting thatfurther is automatically feed into a personal page so they dont have to keep checkingmultiple sites. Its an easy way for students to be kept informed, without beingbombarded.Second Life is a three-dimensional virtual world. There are more than 10 millionusers of Second Life. Hundreds of leading universities and school systems around theworld use Second Life as a vibrant part of their educational programs. Gartner Group [6]is predicting 80% of active internet users will have a second life by 2011. Second Lifeis used by many universities as a promotional tool and to teach real classes to students.Todays students do not prefer to use an online environment like a chat room to meet.Second Life engages distance learners in a way that email, instant messaging andchatrooms do not quite manage. Students can use movements as well as labels to getothers attention, and a user can take all the class with him (her) to another area of thecyberworld. The user can very clearly see who is contributing to the discussions andpaying attention, and who is not. It can bring distance learners together in what feelslike a closer physical relationshipthan other online technologies.Second Life can give them apossibility to attend classes in aworld where the students can fly,change body types, appearance,age, gender and color. Forexample at university lectures youcan be a slim blonde in your 20sby the name of GalaZorca. Athome, you are a little less slim, alittle less blonde, aged 30 andcalledGalinaIvanova.InFig.2. SecondLifeSecondLife we start to talk aboutcyberculture or the way theinternet is transforming our culture without actively participating in it (Fig.2).PodCast - Web lectures and podcasts have become an integral part of universitiese-learning portfolios. They can be created by simply recording a lecture and publishing itinto special client software applications known as podcatchers, for example iTunes.More and more students ask the lessons to be available online. Lecturers can developnew didactic concepts and produce podcasts including not only the lectures, but also- 104 -
e-Learning09additional materials. Students can produce audio-visual content as user-generated elearning objects. YouTube can be used also as a platform for VU 2.0 video lessons.PLE - Concepts like PLE are going to evolve in VU 2.0 [1]. Students will be ableto effectively manage their own online identities, resources and their relationships withmore than one VU. They will be able to select what kind of relationship they want tohave with each VU. Perhaps they can take their first year mathematics courses from VUX, chemistry from VU Y, physics from VU Z, and etc. VU 2.0 is going to become a placefor students to come together, to collaborate and work on common research. Teachingand learning activities will be supported by VU, but they will be managed by thestudents in PLE.EXAMPLES FOR GOOD PRACTICEThe Universities provide some experiences in use of new WEB 2.0 learningtechnologies [12]. We will cite only a few: Digital Ethnography at Kansas StateUniversity [3], Future University, Podcasting University, Ohio University Second LifeCampus, MIT Open Course Ware, Stanford on iTunes U, University of Warwick,Universitat Oberta de Catalunya, BipApp at University of Wisconsin-Madison [10] andetc.The Virtual University of Edinburgh (VUE) [11] is a virtual educational and researchinstitute bringing together all those interested in the use of virtual worlds for teaching,research and outreach related to the University of Edinburgh. VUE has been involvedwith Second Life since 2004 when the first class of students participated in "CampusSecond Life" program. Since this time, VUE students have led a variety of learningexperiences in Second Life, including: conducting open houses for The Second LifePlanetarium, a virtual facility created by Professor Crider for demonstrating the motionsof the stars. Approximately 30 students wrote astronomy shows for the planetarium inFall of 2006.University of Houston design economics course of real live modeling of businesspractices in a virtual world where designers can try their design and entrepreneurialskills against an entire market rather than the code of simulation software. SecondLifeprovides a real time simulation with real time economy and fickle customers.Digital Worlds Institute of University of Florida [4] exists to nurture leading edgeresearch and education between the arts, engineering and the sciences, utilizingadvanced media systems and digital culture. Through the use of interactive tools andtechnologies, the Institute promotes transdisciplinary creativity across classrooms,continents and cultures.Xavier University Web 2.0 strategy [5] is a project that brings into focus the WEBservices as a way of meeting future student needs. Xavier moved the WEB site from avery static and non-student focused one, to a more personalised and active place,where the students published pictures and videos showing what they thought goodabout Xavier and the incoming students graded these to sort out the best which werethen used on the WEB site. Students were also encouraged to build their ownFacebook-like pages to allow them to connect with other students having similarinterests. This approach helps to build points of engagement and connects prospectiveand current students.CONCLUSIONS AND FUTURE WORKThe adoption of VU 2.0 is a complex process. There are some problems that haveto be discussed in the future: How to manage intellectual property in VU 2.0? Whatabout the ownership? How to compete being open? How to manage human resources?
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e-Learning09Ownership: The University owns its research and teaching content. In VU 2.0 theidea is to share with all, so who owns the copyright to the learning content? Copyrightand intellectual property issues involved in WEB 2.0 are not yet entirely clear.Trust: VU 2.0 will change the way people consume information. All forms ofinformation are now available on Internet, this information is getting organised bydifferent people: students, institutions, experts and etc. How to trust any publicationsonline? What about the quality of research and research results?Issue of control: VU 2.0 is challenging copyright because of the open sourceparadigm that allows open access and creation of remix contents. Some questionsabout unsanctioned usage, need of policies and usage guidelines have to bediscussed.VU 2.0 institutions should be active and continually growing and updated, ratherthan a static VU WEB page. VU have to change their content creation and contentdistribution model to remain relevant in a WEB 2.0 World.REFERENCES[1] , ., . , . . eLearning 2.0 ?, , , 2009.[2] A Vision of Students Today http://www.youtube.com/watch?v=dGCJ46vyR9o[3] Digital Ethnography at Kansas State Universityhttp://www.netvibes.com/wesch#Digital_Ethnography[4] Digital Worlds Institute of University of Florida - http://www.digitalworlds.ufl.edu[5] Dood D. W., D. Ruschman. Xavier University's Web 2.0 Strategy: The VirtualLearning Commons, EDUCAUSE Annual Conferences, 2007.[6] Gartner Group - http://www.gartner.com[7] Ivanova, A., G. Ivanova, A. Smrikarov. eLearning 2.0 and Higher Education Possible Implications, Proc. of E-learning Conference, Istanbul, Turkey, 2007.[8] Ivanova, A., G. Ivanova. Net-Generation Learning Style a Challenge forHigher Education, Proceedings of CompSysTech09, Ruse, 2009.[9] Prensky, M. Digital Natives, Digital Immigrants. On the Horizon. NCBUniversity Press, Vol. 9 No. 5, October 2001.[10] Unsworth John. University 2.0, 7th Frankfurt Scientific Symposium, UniversitatFrankfurt am Main, 2007.[11] Virtual University of Edinburgh (VUE)Available online at: http://slurl.com/secondlife/Vue/205/53/30[12] Baytak A. Web 2.0; Open Opportunities for Turkish Universities. AkademikBilisim 2009.ABOUT THE AUTHORSGalina Ivanova, PhD, Principal Assistant, Department of Computing, University ofRuse, Bulgaria, phone:+359 82 888-827, -mail: firstname.lastname@example.com Ivanova, PhD, Principal Assistant, Department of Computing, University ofRuse, Bulgaria, phone: +359 82 888-827, -mail: email@example.com Smrikarov, Assoc. Prof., Department of Computing, University of Ruse,Bulgaria, tel.:+359 82 888-249, e-mail: firstname.lastname@example.com work was partially supported by the European Social Fund and the BulgarianMinistry of Education and Science under the Operation programme Human ResourcesDevelopment, Grant # BG051PO001/07/3.3-02/7.- 106 -
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INTRODUCTIONThanks to enormous progress in available technology new methods of teachingbecame available in recent years. Without a doubt the most important new teachingapproach is e-learning (or in a broader sense distance learning). E-learning givesmillions of people a chance to find better teaching possibilities (or even just the teachingpossibility) than using conventional approach (for example because of a lack of properteaching infrastructure on their place of living). It also gives much broader opportunitiesof adjusting the teaching process for ones limitations (such as time constraints forexample).But it is also important to observe that e-learning cannot solve all problems ofeducation and that it should not be considered as a replacement for the traditional wayof teaching, There are just cases where e-learning suits better to the educationaldemands and in other cases the traditional way of teaching will give better results. Thisobservations lead also to the idea of blended learning composing the teachingprocess from the traditional and the new approach.But this is not the only way in which the newer and the older approach maycollaborate. While it is self understood that the newer one borrows a lot fromexperiences of the older one it is also interesting to note that an influence in theopposite direction is not only possible but it does appear nowadays in many cases. Inthis paper we show on an example taken from teaching at the University of Warsaw thatnot only stationary learning and e-learning may be used together, but also that thetraditional approach itself may be enriched by the use of tools developed solely for elearning.We concentrate here on an example based on object-oriented programmingteaching. There are quite a few reasons for selecting this example. One is the popularityof the object-oriented approach. It is nowadays so dominating in software industry, thatalmost all (or maybe all) higher schools with software engineering curricula havelectures devoted to object-oriented programming or design. Hence such example maybe interesting for many universities. Other important reason for such a choice is the factthat the teaching of object-oriented programming is quite difficult (more on that topic wehave written in [2]) so the exchange of experiences of a broad range of computerscience departments is vital. Next reason for selecting this example is connected to thewide range of possible interactions between the lecturer and students in particular thediversity of possible kinds of assignments given to students. We show here that allthese tasks may be easily handled by Moodle [3] the tool selected here.FEW WORDS ABOUT MOODLEMoodle (Modular Object-Oriented Dynamic Learning Environment) [3, 1] is a freeand open-source Course Management System. It has about 35 thousands of registeredand validated sites in almost 200 countries around the globe with more than 2 millions- 109 -
e-Learning09courses and almost 25 millions of users [4]. It is a web application written in PHP usingMySQL or PostgreSQL database.There is quite a number of available environments for e-learning. They offervarious tools supporting the teacher. These tools may be divided in the followinggroups:
Moodle mainly belongs to the third group, although it has some features of theother two groups.MOODLE IN STATIONARY TEACHINGAlthough Moodle is a tool developed specifically for distance learning it can alsobe used in traditional teaching. But this paper is not about a possibility but about theusefulness of using Moodle in stationary courses. We will examine how Moodle featurescan be used in an example course in Computer Science.As can be seen on fig. 1 Moodle is quite broadly used as a supporting tool forstationary courses at Institute of Informatics of UW (all the listed courses are stationaryones, all but one course categories are for stationary courses). It is our aim to haveMoodle counterparts at least for all regular courses at out Institute. This shows that weare treating Moodle support for our teaching very seriously.
e-Learning09not to describe the entire platform, but only those of its aspects which we have found tobe valuable in traditional teaching.
Ease of collecting home assignments. Students may put them even at nightinto Moodle (but of course not after a previously set deadline). Teachingassistants do not have to search their e-mails (or pen-drives) to find lastversion of solution given by each student.Ease of solving various organizational problems. There are manycircumstances when students want to ask a question after a lecture orcontact hour. Thanks to Moodle's forums they may do that, and since theirquestions and lecturer's answers are publicly available also other studentsmay benefit from it - without asking questions personally or by e-mail.
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INTRODUCTIONThe popularity of distance learning is explained by the fact that there are a numberof indisputable advantages compared with traditional. Distance learning (LMS Learning management system) is a promising type of training relevant to the dynamicsof social and personal needs, one of the most modern educational tools that influencecomplex on the active personality and enhances cognitive activity of students. It is builton principles such as interactivity, self and through self-testing systems, flexibility andpre-negotiated, economic efficiency, openness and dialogue in the planning of training,freedom of choice of time and place, pace and technology training, operationalinteraction with teachers, consultants, associates and partnership in all respects, theopportunity for lifelong learning.SELECTION OF LCMSDuring the selection of LCMS were considered all the results of the investigationphase: established characteristics and needs of students and teachers. We werelooking for a LMS (Learning Management System) or LCMS (Learning ContentManagement System) that support synchronous and asynchronous technologies.Minimal feature set of an LCMS should include [1]:
e-Learning09changes or upgrading. The Shareable Content Object Reference Model(SCORM) is an XML-based framework used to define and accessinformation about learning objects so they can be easily shared amongdifferent learning management systems (LMSs). SCORM is a specificationof the Advanced Distributed Learning. It was designed to facilitate movingcourse content and related information (such as student records) from oneplatform to another, to make course content into modular objects that canbe reused in other courses, and to enable any LMS to search others forusable course content.After functional analysis of the few LMS, CMS and LCMS, the decision was madeto use the open source freeware LMS Dokeos. The main reasons were: Dokeos is freeof charge, appropriateness regarding operating system Dokeos runs on differentoperating systems (including Linux), set of features Dokeos is capable to meet allfunctional requirements, content delivery according to e-learning standards such asSCORM and IMS, feasibility to develop and deploy ones own e-educationalcomponents, Dokeos is continuously evolving.DOKEOS SYSTEM FOR E-LEARNINGDokeos is a web-based open source e-learning system. The code of the system isaccessible to all and can be modified or adapted for specific requirements by anyone.This system also offers a wide range of e-learning tools and provides abundant scopefor creating and organizing interactive multimedia-rich training content and assessment(tests) in an engaging manner. [2]Dokeos offers an efficient user friendly e-learning environment with online contentand assessment creation tools, collaboration tools, as well as sophisticated tracking andreporting tools for monitoring user activity and progress. [2]Training in this platform is organized around a basic unit called training. In eachcourse the teacher is able to organize students in groups. The platform providesconditions for self-sufficiency of each group through the virtual space accessible only tomembers of the group. [3]Each group has a place for sharing different files or links to websites, with a forumthat allows active asynchronous communication, with the wiki, which allows groupediting of a text, with the chat for synchronous communication. The interface is simpleand accessible, and work with these tools is intuitive and requires no special computerskills.Teacher is able to put a special task to group to organize synchronous andasynchronous communication through the forum and chat. Students have theopportunity to leave the documents and work with documents left by other students, togive opinions on the documents and to obtain the opinion of their colleagues.Through the system for testing the knowledge, the teacher is able to monitor thedaily preparation of students.Additional modules, which include the system, are: the curriculum, the ability topublish announcements from the teacher, questionnaires for students, sends privatemessages between students, RSS reader.Figure 1 is presented the main page of the system, which is used for trainingstudents in five disciplines at the Department of Management at University ofEconomics - Varna. [4]
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PHP - for Moodle version 1.4 is required minimum php 4.1.0 or php 5.1.0.For version 1.6 of the system - php 4.3.0 or php 5.1.0. For version 2.0 thereis not support in php4. A minimum requirement is php 5.2.0. MySQL - no requirements to version.The system is available in 78 languages and their variations. As functionality interms of services and amenities, it outperforms the previous two. In some degree, asdisadvantage may be adopted a heavy structure.CONCLUSIONS AND FUTURE WORKIn the academic and business world the term e-learning is used as a new term fordistance learning through internet or web based training.This paper examines, analyses, evaluates and presents the main features of theintegrated information system for e-learning Dokeos. Description of how and where isintegrated this system was produced. Considered are the possibilities of alternativesystems Claroline and Moodle. Defined are their functional features and softwaredependencies. Defined are the advantages and disadvantages to them. The system isavailable in http://corpfinance.eu. As a future work our plans include optimisation,developing and management of this system.E-Learning will become more and more important and pervasive in our lives.ACKNOWLEDGEMENTSThis work is partly supported by projects TRICE 142399-LLP-1-2008-1-BGERASMUS-ENW, BG051PO001/07/3.3-02/21 and BG051PO001-3.3.04/13 with the Bulgarian Scientific Fund.REFERENCES[1] Kastelic M., T. Lonari. A Model of Introducing E-learning System atVocational College for Business Secretaries, Issues in Informing Science andInformation Technology, Volume 4, 2007.[2] http://www.dokeos.com[3] ., . . DOKEOS , - 20-22 2009, , .[4] http://corpfinance.eu[5] http://www.claroline.net[6] http://moodle.orgABOUT THE AUTHORSAssist.Prof. Elisaveta Trichkova, Department of Hierarchical Systems, Institute ofComputer and Communication Systems - BAS, phone: (359 2) 979 2774, e-mail:firstname.lastname@example.com. Krasimir Trichkov, Department of Hierarchical Systems, Institute ofComputer and Communication Systems - BAS, phone: (359 2) 979 2774, e-mail:email@example.com
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INTRODUCTIONERP (Enterprise Resource Planning) systems are comprehensive packagedsoftware solutions that aim for total integration of all business processes and functions.The vendors of fully integrated software offer software that is capable of processing allcommercial functions of any company, no matter how large, diverse or geographicallydisparate the companys components may be. Moreover, the software is not limited tospecific industry sectors: it can be configured for retail industries, mining companies,banks, airlines etc. The major ERP vendors are increasingly targeting small andmedium sized enterprises (SMEs) to generate new sales. Vendors and users are alsomoving beyond core applications to extend ERP systems to support Web-basedapplications, e-commerce, customer-relationship management, and business planning.The growing demand for ERP applications has several reasons, for example,competitive pressures to become a low cost producer, expectations of revenue growth,ability to compete globally, and the desire to re-engineer the business to respond tomarket challenges. Benefits of a properly selected and implemented ERP system canlead to considerable reductions in inventory cost, raw material costs, lead-time forcustomers, production time, and production costs. Researchers (Shanks and Parr in2000) defined ERP implementation as "the process of developing the initial businesscase and planning the project, configuring and implementing the packaged software,and subsequent improvements to business processes". ERP implementation isconsiderably different from any traditional information system implementation for manyreasons [18]:1. The integrated nature of ERP applications causes dramatic changes on work flow,organizational structure and on the way people do their jobs;2. ERP systems are not built, but adopted and this involves a mix of businessprocess reengineering and package customization;3. ERP implementation is not just a technical exercise, but it is a socio-technicalchallenge as it poses new set of management procedures.Many researchers have identified training as a critical success factors in theimplementation of an ERP system because it is expensive, time consuming and itrequires an accurate human resource management.TRAINING IN ERP IMPLEMENTATIONSBy studying the academic literature we identified 26 training methods that can beused in an ERP implementation to support the training activity: Classroom instruction,Print-based material, e-Learning (asynchronous), Conference calls, Email, In person- 121 -
e-Learning09mentoring tutoring, LMS (Learning management systems), Online assessment andtesting, Online references, e-Learning (synchronous), Simulations, Portals, Videobroadcasts, LCMS (Learning content management systems), KMS (Knowledgemanagement systems), Instant messaging, Online mentoring tutoring, EPSS (Electronicperformance support systems), Games, Communities of practice, Wikis, Virtual labs,Chat rooms, Mobile learning, Blogs and Podcasts. Each training method presentsadvantages and disadvantages when applying it into an ERP implementation. Manyresearchers stated that none of these training methods guarantees the success of aERP training program.eLearning Guild research has made a comprehensive study in 2008 (byinterrogating 3000 respondents), regarding the trends in using the above mentionedtraining methods for corporative training. The results are depicted in Figure 1 and areproving that traditional training methods are still widely used compared with thecomputer based training methods. The study used the following rates: (a) 5 points:widely used; (b) 4 points: often used; (c) 3 points: used; (d) 2 points: less used; (e)1points: rarely used; (f) 0 points: not used;Classroom instruction
Elearning asynchronous
Online references
Elearning synchronous
Instant messaging
Online tutoring
Practice communities
Virtual labs
Mobile learning
Trends 2007Trends 2008
Fig.8. Trends in training methods based on the eLearning Guild Research study- 122 -
e-Learning09KOLBS LEARNING STYLE INVENTORYNowadays most researchers favor the idea of personalized training. Inconsequence we choose to focus on Kolbs model (Figure 2) as the concept ofindividual learning style proliferated by Kolb seems to be extensively applied intheoretical and empirical studies. Kolbs Learning Style Inventory (KLSI) includes fourlearning styles [7]: (1) converger, who can be classified as someone who wants to solvea problem and who relies heavily upon hypothetical-deductive reasoning to focus onspecific problems; (2) diverger, who can be classified as someone who solves problemsby viewing situations from many perspectives and who relies heavily uponbrainstorming and generation of ideas; (3) assimilator who can be classified assomeone who solves problems by inductive reasoning and has the ability to createtheoretical models; (4) accommodator who can be classified as someone who solvesproblems by carrying out plans and experiments and adapting to specific immediatecircumstances.Concrete experience
Active experimentation
Reflective observationAC/AE
e-Learning09ERP TRAINING SYSTEM FRAMEWORKStarting from the concepts and observations presented above, we have designeda framework for an ERPTraining system that will help us apply the concept of blendedlearning in an ERP implementation environment. This module is based on a trainingdelivery method that favorites the training process personalization and also delivers theright knowledge in real time for the ERP user. In Figure 3 we present the frameworkadapted to the SFA (Sales Force Automation) module of a Romanian ERP system.ImplementationProject 1SFA database
ImplementationProject 2SFA database
ImplementationProject nSFA database
e-Learning09document the user is processing will be validated in the ERP system only if the datawas introduced in the ERP system according to the training schema standard, otherwisethe document will be invalidated and the user will be informed about the mistakes hemade; Allow the exceptions from the training schemas to be replicated from the ERPsystem database to the ERPTraining database using a replication system; Allow the real time monitoring of the cost and time it takes a consultant to teachan user (for example, if the user wants to participate in tutorial sessions) or the cost andtime a user needs to be trained to use a certain command of the ERP system; Provide the ERP training manager and the organizations management withreports and indicators that show the advances made in the training process in real time.If implemented, this system will offer a series of relevant quantitative measures: Indicators and reports for the organizations management members, that willhelp them:o Have a clearer evidence of the personnel professional quality andefficiency;o Apply training methods adapted to the individual learning styles of theemployees;o Notice if the personnel manifests resistance in approaching new businessprocesses;o Justify in real time the time and the costs for training each employee to usethe ERP system;o Justify in real time the time and the cost for the entire ERP implementationproject; Indicators and reports for the ERP training team and project manager, that willhelp them to:o Monitor each consultant daily activities;o Identify the level of experience each consultant has in a certain domain;o Notice possible inaccuracies in the consultants professional training;o Compare the level of professional training of consultants;o Identify domains in which the company requires additional expertise;o Identify possible improvements in the user training process;o Identify possible improvements in the ergonomics or functionalities of theERP system;o Properly evaluate the time and costs for new ERP implementation projectsbased on previous experience;CONCLUSIONS AND FUTURE WORKThe ERPTraining system framework demonstrates a real possibility of introducingnew features on how to achieve a better knowledge transfer in an ERP implementationenvironment. The system proposes a significantly improved alternative for monitoringthe training activity in an ERP implementation environment, which creates added valuefor both organizations involved in the implementation process. As future work wepropose to design, develop and implement an ERPTraining system adapted to aRomanian ERP system for SMEs.REFERENCES[1] Chapman, David Kolb's learning styles model and experiential learning theory(ELT), Whitepaper, wwww.businessballs.com.[2] Vincent, D. Ross, Personalize training: determine learning styles, personalitytypes and multiple intelligences online, The Learning Organization, Volume 8, Issue 1,2001, Pages 36-43.- 125 -
e-Learning09[3] Weiss, TITAN's Training Makes a Game of ERP, Whitepaper, available at:www.oracle.com, 2007.[4] D. Robotham, Self-Directed Learning: The Ultimate Learning Style?, Journalof European Industrial Training, Volume 19, Issue 7, 1995, Pages 3-7.[5] E. Valkanos, I. Fragoulis, Experiential learning its place in in-houseeducation and training, Development and Learning in Organizations Volume 21, Issue5, 2007, Pages 21-23.[6] E.T.G. Wang, C. C. Lin, J. J. Jiang, G. Klein, Improving enterprise resourceplanning (ERP) fit to organizational process through knowledge transfer, InternationalJournal of Information Management, Volume 27, Issue 3, 2007, Pages 200-212.[7] H. Henke, Learning Theory: Applying Kolbs Learning Style Inventory withComputer Based Training, Course on Learning Theory, 2001.[8]H. Singh, Achieving Success with Blended Learning, Whitepaper,www.instancy.com.[9] H.Tadinen, Human resources management aspects of Enterprise ResourcePlanning (ERP) Systems Projects, Masters Thesis in Advanced Financial InformationSystems, Swedish School of Economics and Business Administration, 2005.[10] I. Scora, The replication mechanism in a Romanian ERP systemenvironment, Revista Informatic Economic nr.1(45), 2008, Paginile 140-146, ISSN1453-1305.[11] J. E Scott, Post-implementation usability of erp training manuals: the user'sperspective, Information Systems Management, Volume 22, Issue 2, 2005, Pages 6778.[12] J. Wu, Y. Wang, Measuring ERP success: the ultimate users' view,International Journal of Operations & Production Management, Volume 26, Issue 8,2006, Pages 882-903.[13] K. Buch, S. Bartley, Learning style and training delivery mode preference,Journal of Workplace Learning, Volume 14, Issue 1, 2002, Pages 5-10.[14] M. Motter-Hodgson, Meeting the Needs of Diverse Types of Learners,CyberCorp Inc's newsletter, 1998.[15] M. Wong, An evaluation on the employees' retraining programmes in HongKong, Employee Relations, Volume 20, Issue 4, 1998, Page 404.[16] N. Manochehr, The Influence of Learning Styles on Learners in E-LearningEnvironments: An Empirical Study, Computers in Higher Education Economics Review,Volume 18, Issue 1, 2006, Pages 10-14.[17] R. E. Yellen, A New Look at Learning for the Organization, InformationTechnology Newsletter, Volume 17, Issue 2, 2006, Pages 29-31.[18] She-I Chang, ERP Life Cycle Implementation, Management and Support:Implications for Practice and Research, Proceedings of the 37th Hawaii InternationalConference on System Sciences 2004.ABOUT THE AUTHORSAssistant Iuliana Scora, PhD candidate, Department of Economic Informatics,Academy of Economic Studies of Bucharest, -mail: email@example.com Anca Andreescu, PhD, Department of Economic Informatics, Academy ofEconomic Studies of Bucharest, -mail: firstname.lastname@example.com Adina Uta, PhD, Department of Economic Informatics, Academy ofEconomic Studies of Bucharest, -mail: firstname.lastname@example.com
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INTRODUCTIONThe actuality of the problem of e-learning (particularly distance learning) wasprompted mainly by the globalization of society worldwide, and by the incredibly highrates, which are developing communication technologies, especially the global networkInternet. The aim of distance learning is to adapt existing teaching approaches andresources for learning and communication between trainers and learners to modernelectronic means and methods for virtual communication between people [2,7].There are some projects for polymer e-learning consist of different types of toolsfor performing e-learning (Excel Macros) which are suitable for education of topics suchMaterials Rheology, Processing Methods etc. [10]. Some authors use the attractivetechnologies such Flash Active Content and Shockwave Objects to illustrate some basicprinciples of polymer synthesis and processing. The main characteristics of the polymermaterials are described too [11].There are some electronic content that covers some aspects of polymer education(the main part is video content) [12]. There is and a good source of polymer contentsuitable in university education [13]. Some interesting projects covering story of polymerscience and glossary with main terms explanation are available on [14].The interest may be focused and on organization like POLYED that is aconsortium of groups interested in science education in general and polymer educationin particular [15]. Information on plastics used in our modern world is available on [16].All of the described projects use a web tools in the development process buttheres some difficulties when they are used for the real experiment simulations. Theneed an Internet connection too.The purpose of this work is a development of application software suitable forincorporation as a module in a electronic-based training system for conducting elearning in the field of technical sciences in particular polymer technology. Thedeveloped computer programs are Internet independent applications which everyteacher can use on a single computer not connected to others in a network. For theirexecution computer programs need only installed operating system on the computer.LAYOUTThe proposed computer programs were developed using the environment for rapiddevelop of application programs MS Visual Basic. NET Standard Edition, designed byMICROSOFT Corporation, which contains within it the tools that allow to significantlyspeed up the process of development and creation of application software [4,8,9]. Thechoice of MS Visual Basic is by the following reasons:
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e-Learning09The code and the program in general are optimized for operation of each computersystem operating in terms of Windows XP/2000 operating system with installed .NETFramework.The program is based on a huge experimental material obtained investigatingpossibility of phosphogypsum using as light mineral filler for polymers [1, 3]. The mainused approach is systematic collecting of experimental data and their processing withan author developed programming tools (statistical and experimental design modules).After these steps developed equations are turn into algorithms used in the subsequentprogramming process.The second program developed concerns the possibility of conducting a simulatedstudy of the process of ionic-coordination polymerisation of ethylene (polymerisationusing the catalysts of Ziegler - Natta) [5, 6].The polymerisation is a process that requires a long and strict observance oftechnological parameters for its implementation. Some of technological conditions areslightly dangerous for students too. Therefore, in developing methods for its study isnecessary to resort to search for opportunities to facilitate the visualization of theparameters affecting it.The developed demonstration computer program allows monitoring of the effectsof seven parameters influencing the progress and products of ionic-coordinationpolymerisation of ethylene.The technological parameters are:1. Mixing frequency, rounds/minute;2. Reactor temperature, C;3. Heating speed, l/min;4. Catalyst amount, g;5. Isobutylene amount, vol. %;6. Hydrogen amount, vol. %;7. Ethylene amount, depending on pressure, MPa.In calculating process of the characteristics of the product of polymerization polyethylene have been used different functional dependencies.The part of source code for the final parameters calculation written in Visual Basicis shown below in Listing 1 (see Appendix):It is very important to mark that the simulation of the ionic-coordianationpolymerisation can work in two different conditions: permanent or variable. Dependingon the process state the final results obtained by students have different values.The parameters whose effect is examined by the proposed program arerepresented by labels and text boxes located adjacent to them. The values that theyrepresent introduced through sliders in a limited space ahead. There is a possibility foreliminating errors by the student which may occur when entering unbecoming values.The determination of baseline parameters is by pressing the Calculate button. It isalso possible to check the zero input values for certain parameters, which may lead todifferent programming errors (e.g. division by zero if the parameter involved in thedenominator as a dependency).In this case the user is prompted for entering of right value. To the text boxshowing the values of the yield of polymer, the density and melt index of progress, arebands that give a visual idea to what extent the resulting values are compared to theminimum and maximum values for the parameter.The main window of the program and has an appearance shown in Fig. 3:
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Fig.3. Main window of the simulation application for the process of ionic-coordinationpolymerisation of ethyleneThe final stage at this process examination can be the investigation of the optimalvalues for technological parameters by experimental design methods. For this purposedifferent type of statistical software can be used.CONCLUSIONS AND FUTURE WORK1. Demonstration computer programs simulating the stress-strain tests ofreinforced polymers and ionic-coordination polymerization of ethylene have beendeveloped.2. Using available software and modern approaches for developing andprogramming design modules have been created, providing basic functionality forincorporation within the training system for polymer science education.3. The developed modules are subject to a logic embedded in their functionality,which is a guarantee for their excellent timing in the course of their joint work within thee-learning system.4. The developed computer programs make possible education within two fields ofpolymer science which requires many hours experimental work that is impossible in thelaboratory learning conditions.5. The proposed computer programs are used experimentally in polymer educationof Department of Material Science, Prof. Dr. Assen Zlatarov University Burgas,BULGARIA.REFERENCES[1].Denev G., Polymer Compositions filled with phosphogypsum and itsmodificates, First International Conference Technomat 97, 1997.[2].Denev Y., A. Popov, G. Denev, Modules for creation of Web-based educationsystem, Proceedings of Scientific Conference with International Participation StaraZagora 2005, v.5, p.303[3].Denev Y., G. Denev, A. Popov, Utilization of phosphogypsum like a mineralfiller for elastomers, International Scientific Conference Bulgarian Association RubberIndustry, 18-20 October 2007- 131 -
e-Learning09[4].Petroutsos E., Mastering Microsoft Visual Basic 2008, Sybex, 2008.[5].Kafarov V., Cybernetics Methods in Chemistry and Chemical Technology,Chemistry, Moscow, 1971 (in Russian).[6].Kurenkova V., Laboratory Experiments in Polymer Science, Chemistry,Moscow, 1990 (in Russian).[7].McCormack C., D. Jones, Building A Web-Education System, John Wiley &Sons, 1997.[8].Petroutsos E., Mastering Visual Basic .NET, Sybex, San Francisco, 2002.[9].Roman St., VB .NET Language in the Nutshell, OReilly, 2001.[10].http://ptlonline.org.uk[11].http://www.plastics-elearning.com/[12].http://www-sul.stanford.edu[13].http://www.chemheritage.org[14].http://www.pslc.ws[15].http://www.uwsp.edu/chemistry/polyed/[16].http://www.americanchemistry.com/plastics/APPENDIXListing 1P = txtP * 100D = J + (1 + Q) / ((0.01 * txtB + 1.05) * Q +S = ((P * txtC) / (203 + txtR * 1000) + 1) * 0.5)txtT'Check <>If S > 102 ThenIf n < 10 ThenMsgBox "Lower Temperature",n = 10vbExclamation, "Correction"End IflblT.ForeColor = vbRedIf n > 100 ThenEnd Ifn = 100J = (P - P * 42 / S) * txtC * (1 - 0.005 * (txtHEnd If+ txtB))If J > 15 ThenJ = J * (0.002 * txtY - (0.001 * txtY) ^ 2 +J = 150.001)End IfIf P / J < 1.3 ThenIf D > 0.95 ThenMsgBox "Lower Temperature",D = 0.95vbExclamation, "Correction"End IflblT.ForeColor = vbRedtxtN.Text = nStoptxtJ.Text = JEnd IftxtD.Text = DQ = 0.94 * J / txtCn = Q * txtCJ = (0.0007 * S / 4 - 0.01)
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INTRODUCTIONNowadays it is widely recognized that Technology Enhanced Learning /TEL/ has toprovide information and create learning situations, enabling activities for constructinglearners knowledge by the learner himself with the teachers or peers assistance [1].Current TEL systems and tools facilitate the development of various learning situationsby exploiting specific characteristics of the learning process and its participants,reducing substantially the time and space limitations. In classical education as well as ineLearning activities learning content has always been regarded as keystone for alllearning situations. Understandably the authoring of learning materials is one of themost important and labour-intensive activities in the modern TEL practice. The presentpaper discusses means to facilitate the authoring and the access to learning materialsby exploiting their content- and context-sensitive presentation, based on the work in ajust finished 6FP IST project LOGOS Knowledge-on-demand for Ubiquitous Learning.It also presents some observations on the project results and a direction for futureresearch.LOGOS PLATFORM ARCHITECTUREThe project LOGOS /2006-2009/, developed by 15 partner organizations from 8countries addressed innovative developments for all the e-Learning processes components resources, services, communication spaces. The primary target group of theproject consisted of eLearning actors (authors of courses, lecturers, advanced learners,learning content providers), creating learning materials for university students and/or foradult learners in non-formal and informal education. The secondary target groupconsidered eLearning providers (companies and training institutions) and technologyproviders (DTV-companies, mobile operators), interested of cross-media delivery oflearning materials. The project main objective was to create a platform for ubiquitous(any place, any time, personalized) learning which combines:1/ a subsystem, called Authoring studio for creation of learning materials fromexisting digital repositories by semantic annotation and access.2/ facilities for cross-media courseware delivery through digital video broadcasting,mobile and IP-based communication channels.The architecture of the LOGOS authoring studio [3] is based on the followinghierarchy of data models for the information objects:
e-Learning09Learning Designers - they create abstract learning scenarios for dynamicdevelopment of personalized courseware.- Courseware Developers - they create, maintain and publish courseware forlearners.The Architecture of the LOGOS platform is shown on Figure 1. Its main blocks
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e-Learning09CONCLUSIONS AND FUTURE WORKThe experimentation with the LOGOS Authoring studio supports the followingconclusions:
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e-Learning09ACKNOWLEDGMENTThe work on this paper was funded partially by the project No 142399-LLP-1-2008-1BG-ERASMUS-ENW (ETN TRICE) and the NSF project D-002-189 SINUS SemanticTechnologies for Web Services and Technology Enhanced Learning.REFERENCES[1] Aceto S., C. Delrio, C. Dondi (Eds.). E-Learning for Innovation. ExecutiveSummaryofHeliosYearlyReport2007,ISBN:2-930429-13-5,p.25,http://www.education-observatories.net/helios (last accessed August 2009)[2] Agre G., D. Dochev - An Approach to Technology Enhanced Learning byApplication of Semantic Web Services. Cybernetics and Information Technologies,Vol. 8 (2008), 3, pp. 60-72.[3] Arapi P., N. Moumoutzis, M. Mylonakis, S. Christodoulakis. A Framework andan Architecture for Supporting Interoperability between Digital Libraries and eLearningApplications. Proceedings of the DELOS Conference on Digital Libraries, Pisa (Italy),February 2007.[4] Chein M., A. Gutierez, S. Lalande, V. Saraydarova, K. Staykova. Tools andMethodologies for Ontology Design and Digital Objects Annotation using ConceptualGraphs. In: Szucs A. (Ed.) Proc. of LOGOS Open Conf. New Technology Platforms forLearning Revisited. Budapest, January 2009, pp. 47-61.[5] Dietze, S., A. Gugliotta, J. Domingue. Towards adaptive E-LearningApplications based on Semantic Web Services. In: Proc. of TENCompetence Workshopon Service Oriented Approaches and Lifelong Competence DevelopmentInfrastructures, January, 2007, UK.[6] Marinchev I., I. Hristov. Managing Learning Objects in Large Scale CoursewareAuthoring Studio. In: Christodoulakis S. (Ed.) Proc. of Workshop on "Cross-Media andPersonalized Learning Applications on top of Digital Libraries" LADL 2007, Budapest,September 2007, pp 101 109.[7] Ovchin E., Sh. Militello, E. Suba, A. Waterson. L. Pemberton, R. Griffiths. AFormative and Summative Usability Evaluation Study of a Cross-platform E-LearningAuthoring Environment. In: Szucs A. (Ed.) Proc. of LOGOS Open Conf. NewTechnology Platforms for Learning Revisited. Budapest, January 2009, pp. 20-36.[8] Paneva-Marinova D., L. Pavlova-Draganova, L. Draganov, R. Pavlov, M.Sendova. Development of a Courseware on Bulgarian Iconography for Ubiquitous Ondemand Study. In: Szucs A. (Ed.) Proc. of LOGOS Open Conf. New TechnologyPlatforms for Learning Revisited. Budapest, January 2009, pp. 37-46.ABOUT THE AUTHORSAssoc. Prof. Danail Dochev, PhD, Department of Artificial Intelligence Knowledge Processing, Institute of Information Technologies Bulgarian Academy ofSciences, Phone: +359 2 870 75 86, -mail: firstname.lastname@example.com. Prof. Radosalv Pavlov, PhD, Department of Computational Linguistics,Institute of Mathematics and Informatics Bulgarian Academy of Sciences, Phone:+359 2 979 38 31, -mail: firstname.lastname@example.com.
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INTRODUCTIONConcept maps could be good tool to view the content and specify the requirements ofthe knowledge flow in the learning applications. Also the concepts and the relations canbe seen at first glance. These benefits of the concept maps show that make the systemview more apparent for learners. The concept map approach is categorizing knowledgein the information hierarchy. Learners may understand the knowledge domain whichrepresented in a way that resamples the thinking of the subject matter experts (SMEs).This suggests that when developing an e-learning application, a predesigned conceptmaps can be useful. Coffey, describes the usage of concept maps to create a coursedescription based on the idea of an advance organizer. This method is different thenlinear sequences of topics which typically found in traditional ways [1]. Novak and Iuliclaims that use of concept maps as meta-cognitive tools help to think easily [2].In the higher education, engineering students have some difficulties when theyhave need structuring their thoughts in a knowledge domain that they are not used.They also often demanded to use their learning skills to create thinking processes andcommunicate the ideas using learning media that are not appropriate for all learners.When eliciting requirements of individuals in a learning environment and structuringknowledge representations in a well defined manner, concept maps serve a good tool toview the advance organization of the learning contents flow in the applications.A well designed concept map can be very helpful to improve and systemize thelearning processes of these learners and support their learning in unaccustomedknowledge domains. But sometimes the concept maps can be too complex tounderstand. Thus students fail to follow all the relations on the nodes of the maps. Sowhen taking the different learner requirements into account to provide the wholeconcepts at once cannot be a solution all the time. The usage of concept maps thatserve learning styles is important to cover all the user requirements. Because, differentlearners have different learning preferences when study in these concepts. According totheir learning styles some of them may prefer to see and learn the whole concepts atonce but others may prefer to see and learn the concepts within steps. Anotherconsideration point is that some of the learners needs the visual aids to learn theconcepts but others not.In this study, meta-cognitive tool development stages are detailed to share someexperiences.BACKGROUNDUniversity students have some difficulties when creating relations between ideas,knowledge and integrating the information with their prior knowledge [3]. A welldesigned concept map can be helpful to see the structure of the content at a glance witha visual presentation that fosters learning of complex information more quickly ratherthan complex and heavy information loaded presentation of words and verbal contexts.- 139 -
e-Learning09Because of the ease in receiving information for later retrieval, the concept maps arevery useful for educational purposes. Another reason that concept mapping is beneficialfor the learning is that it serves as a kind of template to organize knowledge and tostructure it [4]. Plotnick suggested that some other usages of the concept maps usagehelpful in areas such as creativity, complex structure design, learning assessment,brainstorming, and communication of complex ideas [5]. Also there is a possibility ofemploying the concept map as an advance organizer [3]. An advance organizer is aglobal overview of the material that is to be learned [6, 7]. The concept map tools canbe also in a computerized form to foster students learning. A learning style is themethod that allowing individuals to learn more comfortable. Although there is no perfecttheory accepted by researchers for the learning styles [8]; implementing a learning stylemodel can be useful in teaching/learning if it matches the learning modes of thestudents. Felder explains that there are non-matching learning styles of engineeringstudents and traditional teaching styles of engineering professors [9]. In this situation,students get bored and they become inattentive in class. Also they achieve lower scoreson tests, and get discouraged about the courses, the curriculum or themselves. In thebeginning, they discussed the aspects and dimensions of learning style significance inengineering education. They were saying that there are learners as Sensing andIntuitive Learners; Visual and Auditory Learners; Inductive and Deductive Learners;Active and Reflective Learners and Sequential and Global Learners. Recently theydropped the/deductive dimension, and changing the visual/auditory category tovisual/verbal. Felder describes active learners shortly as to retain and understandinformation best by doing something active with it discussing or applying it orexplaining it to others. Reflective learners prefer to think about it quietly first [10];sensing learners tend to like learning facts, intuitive learners often prefer discoveringpossibilities and relationships [11]; visual learners remember best what they seepictures, diagrams, flow charts, time lines, films, and demonstrations [11] and sequentiallearners tend to gain understanding in linear steps, with each step following logicallyfrom the previous one. Global learners tend to learn in large jumps, absorbing materialalmost randomly without seeing connections, and then suddenly getting it [9]. Withinthe usage of software applications and answering learning styles, the concept maps canbe an important tool to get the meaningful learning for the students learning. In thedevelopment of such software, students also get control of their learning process bygetting the advantage of learning opportunities which technology offers within theinstruction [13].COURSE DESIGNThe instructional designer should consider the arrangements of course contentand materials in a well structured manner to create useful course material. Designing ofconcept maps could help both the instructor and the instructional designer to see theentire course in a big and detailed view. This system view helps them to represent therelations between contents and information. Also the concept maps can be useful fortracking the content of the course. The map should be designed by a person who hasstrong knowledge about a given topic in a specific context. There are no correctconcept map on a specific content, as there can be many different representations ofthe topic that are correct. To create such strong concept maps; an instructor or aninstructional designer should use a software tool to make the process much quicker anduseful. There are some commercial tools are available at the market [13].In this study, F/OSS FreeMind (http://freemind.sourceforge.net/) was used tocreate the visual presentation of the concept maps. We selected this software becauseit is user-friendly especially for the instructors. By the instructional designer anddeveloper side, the ability of exporting the data of the visual representation of theconcept maps outside of the program in the XML format is crucial. The concept maps- 140 -
e-Learning09basically can be created to prepare knowledge representation in a form of a graphwhich is shaped within boxes consists of knowledge texts that have connections bylabeled arrows [14].The concept maps had been created by the two subject matter experts in thelearning domain. First SME is the instructor of the History of Civilization course andthe other is an expert at the history and art. They linked and create the relations foreach node for the visual materials and additional course materials that are selectedfrom the visual galleries related to the course. Concept maps were limited with threelevels because information must be learned at working memory and it has a capacity ofabout four chunks in young adults [15]. SMEs completed the concept maps and thenthey reviewed the maps and discussed the any missing parts such as lack ofinformation. Finally an instructional designer reviewed the concept maps and linkedmaterials for their inner structure of accuracy according to the relatedness of the courseobjectives. Concept maps provide an initial conceptual frame for learning and thisallows the usage of concept maps as a schema for the structured course. In our study,the presentations were constructed according to these schemas. To link visual materialssuch as historical maps, illustrations and pictures for each concept also should beincluded into presentations. All the participant students of the course have studied thepresentations in their class hours. Presentations were designed in a linear way; so allthe contents were presented sequentially. All the learners learn through thepresentations and examples in this order and the order was not changed. This kind ofthe structure does not adapt to individual learners and cannot support different learningstyles.THE SPECIFICATIONS OF META-COGNITIVE TOOLA learning tool has been developed to get the contents to fit exactly within conceptmaps so the learners can have a chance to examine the lessons of the course in anorganized way that was indicated also in concept maps. When developing such a tool,there are some encountered constraints. Such as,1.The tool should provide lessons and related materials in a way that matches withinconcept maps2. The tool should give the presentations of the contents both in sequential andglobal layouts3.The tool should give the presentations of the contents both in verbal and visual.4.The tool should provide a navigation that is easy to learn and use.The learning tool designed to cover the requirements stated above. Whendeveloping this kind of learning tool it was essential to access both in class during thelecture hours and online for the future usage as an e-learning tool. Also the portability ofthe learning tool was an important issue. For this reason, the Adobe Flash frameworkwas used. Both PCs and Macintosh based systems can easily connect to applicationwithout need of having to connect to Internet. Another reason of selecting Flash was tohave a capability compatibility of XML. While developing the learning tool there was acrucial requirement to provide lessons and related materials in a way that matcheswithin concept maps. To handle these requirements the presentation contents was usedmatching exactly same of the concept maps by using the XML which included the dataof the visual representation of the concept maps that were exported outside of theprogram. The learning tool just loads XML automatically and programmed to analyzeloaded data to present related materials in visual and verbal forms that were indicatedby the SMEs within concept maps. The figure 1 demonstrates the essential screens ofmeta cognitive learning tool. At the top of the screen, there is a toolbar which has threecombo box menus to access the visual concept maps that are interpreted from theconcept map data. The label 1 indicates the menu of pictures. The tool bar below is for- 141 -
e-Learning09navigations. At the bottom of the screen, there is an information collection purposebutton which may be used for accessing helps and documentation of the learning tooland its usage info. The button next to it is for accessing other chapters list via a popupscreen. When user jumps to other chapters of the learning tool, it loads the related dataof the concept maps by using XML format. The buttons labeled with arrows and may beused to move forward and backward in the program. The learners can navigate in theapplication by two different ways one is sequential and other branched web. If a userlearn in a sequential steps it has chance to use linear model, if conforms in global theycan switch to branched web. To access the branched web navigation the user shouldgo to content map form upper menu and move in the concept maps. When userswitches concept maps to navigation, the main screen and navigation buttons alsochanges as the prev./next buttons replaces within a -/+ button set for zooming control ofthe maps. Also vertical and horizontal scrollbars are included to move in this conceptmaps. When in sequential mode has been selected the overall relations of the conceptsat nodes has been deleted to ensure the sequential learner have easy with following theconcepts.
e-Learning0918]. In this study, only the visual/verbal and sequential/global dimensions were analyzedbecause of the learning tool is designed to cover these dimensions only.Phase 1: The Classical Power Point PresentationsIn the first phase, three chapters of History of Civilization course has beenprepared by sticking to concept maps in the power points slides which designed by theSMEs. Each chapter takes six class hours and it has been given by the lecturer byregarding the contents of concept maps. After completing the classical lessons withpresentations that based on concept maps, an online assessment with 45 itemsemployed to students whose were attending all the lessons with power pointpresentations within 60 minutes time limit. The questions also were prepared by theSMEs according to the concept maps and power point presentations. After phase 1,students were offered to use the concept map tool in all lessons and ordered tocomplete the LIS inventory when attending to History of Civilization online course portal.These students were administrated the 44 itemed ILS instrument. Even though therewas no time limit for completion, all the students finished it within average 60 minutes.Phase 2: Meta-Cognitive ToolIn the second phase, the other three chapters of History of Civilization lessonshave been prepared also sticking the concept maps. Then all the chapters areadministrated by using the new developed module of online course management tooland the concept map based learning tool implemented both for the online and in classusage. After then, students were introduced the usage of the learning tool and theyexplored the tool freely in class for 10 minutes. They also recommended for trying thetool within online system. After a week later, the students get accounted to the tool, thelectures takes six class hours for each given with using concept based tutorial tool. Aftercompleting lessons with using the concept map based tool, another online assessmentwith 45 items employed to the students who was attending all the lessons with conceptmap based tutorial tool presentations in class or online within 60 minutes time limit. Thequestions also were prepared by the SMEs according to concept maps information. Toensure to have the same difficulty for the course content and the online assessmentquestions, the SMEs whitened in a dependent rating score.CONCLUSIONSAs seen in figure 2, the students who get scores above the average of 55 points(std.dev.=18) of 100 points grades alsoachieve well and can get scores abovethe average of 60 (std.dev.=15) pointsout of 100 points. Difficulty of items hasbeen balanced with the assignments ofthe both studies by the SMEs. We cansuggest that students who are aboveaverage, they can benefit from the metacognitive tool. The students who are ataverage level at both studies are gettingdifferent scores that are unbalanced andunstable. Some of the students obtainingFig. 2. Benchmarking the Power Pointimprovements but others show fails. OnLectures versus Meta-Cognitive Toolthe other hand, students that far belowthe average are gets significant fall. The results shows that when developing a conceptbased learning tool, the students that can manage their learning by themselves and gethigher scores in the classical power point based learning also gets higher scores andthe usage of tool improves their performances and achievements. But the students whoget average scores shows non-stable and significant improvements using the Meta- 143 -
e-Learning09cognitive tool. Students who are low achievers and far below the average of the firststudy cannot benefit the tool and their usage of the tool decreases their performance.The usage of such tool instead of classical PPTs within the control of the lecturer at thefirst hand cannot be suitable for low achievers. The results show that meta-cognitivetool that based on a concept map methodology is very useful and the addressinglearners needs of achiever students above the average.REFERENCES[1] Coffey, J. W. A meta-cognitive tool for courseware development, maintenance, and reuse.Computers & Education, 48 (2007), 548566.[2] Novak, J. D. & Iuli, R. J. ,1991. The use of meta-cognitive tools to facilitate knowledgeproduction. The Fourth Florida AI research symposium (FLAIRS 91), Pensacola Beach, FL, May, 1991.[3] Novak, J.D., & Gowin, D.B. ,1984. Learning How to Learn. New York: Cambridge UniversityPress.[4] Novak, J.D. ,1998. Learning, Creating, and Using Knowledge: Concept Maps as FacilitativeTools in Schools and Corporations. Hillsdale, NJ: Lawrence Erlbaum Associates.[5] Plotnick, E. ,1997. Concept Mapping: A graphical system for understanding the relationshipbetween concepts. ERIC document, June 1997, EDO-IR-97-05[6] Ausubel, D. P.,1968. Educational Psychology: A Cognitive View. New York: Holt,Rinehart andWinston.[7] Ausubel, D. P., Novak, J. D. & Hanesian, H., 1978. Educational Psychology: A Cognitive View(2nd ed.). New York: Holt, Rinehart and Winston.[8] Brumby, M.N. Consistent Differences in Cognitive Styles Shown for Qualitative BiologicalProblem-Solving, British Journal of Educational Psychology (52:2), J u n e 1982, pp.244257.[9] Felder, R.M. & Silverman, L.K. Learning and teaching styles in engineering education.Retrieved April,8,2007 from http://www.ncsu.edu/felder-public/Papers/LS-1988.pdf[10] Felder, R.M.,1994, Meet Your Students: 5. Edward and Irving . Chemical EngineeringEducation, 28(1), 36-37 (Winter 1994).[11] Felder, R.M.,1989. Meet Your Students: 1. Stan and Nathan. Chemical EngineeringEducation, 23(2), 68-69 (Spring 1989).[12] Grabe, M, & Grabe, C.,2001. Integrating Technology for Meaningful Learning. HoughtonMifflin Company: USA.[13] Caas, A. J., Carff, R., Hill, G., Carvalho, M., Arguedas, M., Eskridge, T. C., Lott, J., &Carvajal R. Concept Maps: Integrating Knowledge and Information Visualization. Retrieved April 8,2007from http://cmap.ihmc.us/Publications/ResearchPapers/ConceptMapsIntegratingKnowInfVisual.pdf .[14] Safayeni, F., Derbentseva ,N., J. Caas, A. Concept Maps: A Theoretical Note on ConceptsandtheNeedforCyclicConceptMaps.RetrievedApril8,2007fromhttp://cmap.ihmc.us/Publications/ResearchPapers/Cyclic%20Concept%20Maps.pdf[15] Cowan, N. ,2001. The magical number 4 in short-term memory: A reconsideration of mentalstorage Capacity. Behavioral and Brain Sciences, 24 (1).[16] Felder, R.M., Soloman, B.A., Index of Learning Styles. Retrieved April 8,2007 fromhttp://www.ncsu.edu/felder-public/ILSpage.html .[17] Litzinger, T., Lee, S. , Wise, J. & Felder, R., 2005. A Study of the Reliability and Validity of theFelder-Soloman Index of Learning Styles . Proceedings of the 2005 American Society for EngineeringEducation Annual Conference & Exposition .[18] Zywno, M. 2003.A Contribution to Validation of Score Meaning for Felder-Solomans Index ofLearning Styles. Proceedings of the 2003 American Society for Engineering Education AnnualConference & Exposition
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INTRODUCTIONNowadays mobile devices become more widespread and are increasingly used formany purposes. Fewer customers worldwide use mobile devices only as a singlefunction unit. Gradually these devices become a platform for a wide range ofapplications - shooting and sending photos, listening to music, watching video, receivingGPS information, obtaining information from Internet and others. The mobile devicesalso provide an opportunity for the realisation of mobile learning.User Interface (Human Machine Interface) includes the methods by which theusers interact with a device. The user interface provides two basic options [8]:
Input, through which users can control and interact with a device; Output, which reflects the users actions.The development of user interface for applications for mobile devices - cellphones, smart phones, PDAs (Personal Digital Assistants) and super phones deservesspecial attention, as they have some limitations, most important of which are lessscreen resolution and smaller keyboard than the desktop computers [6, 7]. This is thereason why the user interface for mobile applications (Fig.1), including these for mobilelearning, should be well designed and implemented.
Graphical user interface (GUI) takes users input information through variousmobile computer keyboards (virtual, mini-QWERTY, foldable) and/or pointingmethods which react to the screen. The GUI displays relevant information on- 145 -
e-Learning09the screen of the mobile devices. Web-based user interface accepts a user request, transmits it to web server,receives response and displays the information on the mobile device screenusing mobile web browser.2. User interface designUser interface design or user interface engineering is the design of computers,appliances, machines, mobile communication devices, software applications, andwebsites with the focus on the user's experience and interaction [8]. The main purposeof the traditional graphic design is to make a product attractive while the goal of the userinterface design is to facilitate the user-application interactivity and to increase theeffectiveness of the users work.Development of user interface for mobile application is a part of the overall processof design and development of a separate mobile learning application or of completemobile learning management system [3]. Most often at this stage a graphic designer, auser interface designer and a programmer work together.There are several iterative stages during the development of the user interface formobile applications:
a)Fig.3. Most used navigational schemesa) for personal computers; b) for mobile devices
These popular schemes are not applicable to mobile devices since they have asmall screen, small keyboard and limited methods of navigation.Preferred method during the development of mobile navigation schemes for mobilephones is using a numbered vertical list of options which are associated with thecorresponding numbers (0-9) from the phones keypad [1].- 147 -
e-Learning09During the development of navigation for PDAs or smart phones more modernmethods of navigation can be applied. However, the orientation of the screen should betaken into account, as most of mobile devices still work in a vertical mode and theycannot display text in several columns. It is recommended that the navigation schemesupport two fields with navigation - at the top and bottom of the screen (Fig.3b) and theinformation is visualised between them [1].5. Requirements for realisation of user interface for mobile learningapplicationsThe following requirements to the user interface of mobile learning applicationscan be defined:Regarding the navigation:
The navigation should be intuitive, located at the top and/or bottom of thescreen and must support work with mobile device keyboards. To reduce re-visualization of the main menu and submenus of each screen(page) it is appropriate to place them on individual screens and the navigationmust support links to these screens. The navigation buttons should be large enough to work with them using stylusor touch screen. The navigation elements should be visualised in the same way on mobiledevices with different screen resolution. It is better to provide a choice of several options, rather than to enter the text[6].Regarding the multimedia elements:
The length of the text must be consistent with the size of the screen and notmore than 3 times the height of the screen [6]. Horizontal scrolling of the text must be avoided. A possibility to change the font size must be provided. This will give anopportunity to users to increase or decrease the font size when it is needed, asthe text with small font size is difficult to read [4]. Due to the lower screen resolution serif text fonts should be used. The text should be easily reading and for this purpose it should be contrast onthe background. The size of graphic images (in pixels) must be consistent with the size of thescreen. It is appropriate to separate graphics from the text - for example on separatescreens (pages) which to be displayed only by users request. This will increasethe speed of loading pages containing mainly text information. If the application includes audio and it is intended to be controled by the user, itis appropriate to provide a separate screen on which the control elements to bearranged. If the application includes video a separate screen for its reproduction also hasto be provided.Regarding the verification of users knowledge:
e-Learning09of the time that is given to the same tests, but performed on personalcomputers. In literature source [2] is determined that the text entry speed inmobile devices is about 3 times lower than that of PCs.6. Realisation of user interface for mobile learning applicationsThus defined requirements are used in the realisation of the user interface formobile applications for teaching the course "Multimedia systems and technologies.These applications are developed using Adobe Flash.Fig.4 shows screens of a mobile multimedia application for training on theme"Audio". On Fig.4a and Fig.4b is shown the ability to change text font size. On the samescreens there is a link to other screen (Fig.4c) on which the user can see the imageassociated with the text.
a)b)c)Fig. 4. Screens from multimedia mobile learning application AudioFig.5 shows relevant screens for audio control (Fig.5a), for video control (Fig.5b)and for testing students knowledge (Fig.5c).
a)b)c)Fig. 5. Screens from multimedia mobile learning application AudioCONCLUSIONS AND FUTURE WORKThe user interface for mobile devices is crucial in the development of mobilelearning applications. The well designed user interface of a mobile application can- 149 -
e-Learning09attract customers while the good functionality and learning content can keep them touse it.The proposed methodology and the defined requirements can be used fordevelopment of a graphical user interface as well as for development of web-basedinterface for mobile learning applications.They are practically implemented during the development process of series ofmultimedia mobile applications for training students at the University of Ruse in thesubject "Multimedia Systems and Technologies".REFERENCES[1] Cremin, R. et al. DotMobi Mobile Web Developer Guide, mTLD, Ireland, 2007.[2] Georgiev, T., E.Georgieva. Investigation of the Text Entry Speed and Accuracyin Mobile Devices, Proceedings of the CompSysTech2009, Rousse, Bulgaria, June 1819, 2009.[3] Georgieva, E., A.Smrikarov, M.Teodosieva. Mobile Learning SystemsDevelopment Methodology. Proceedings of the Second International e-LearningConference, September 6-7, Berlin, Germany, 2005. Gent, Belgium, 2005, pp.135-147.[4] Gong, J., P.Tarasewich. Guidelines for Handheld Mobile Device InterfaceDesign, http://www.ccs.neu.edu/home/tarase/GuidelinesGongTarase.pdf[5] Hussain, Z. et al. User Interface Design for a Content-aware Mobile MultimediaApplication: An Iterative Approach, MoMM 2007 & iiWAS 2007 WorkshopsProceedings, pp.115-120.[6] Tobar, L.M. et al. WebA Mobile (Web Analysis Mobile): Assistance Tool for theDesign and Evaluation of Websites for Mobile Devices. New Trends on HumanComputer Interaction, Springer-Verlag, 2009, pp.127-139.[7] Tselios, N. et al. Design for Mobile Learning in Museums, Handbook ofResearch on User Interface Design and Evaluation for Mobile Technology, Volume 1,Information Science Reference, USA, 2008, pp.253-269.[8] User Interface and Design, http://www.scribd.com/doc/2409206/User-InterfaceDesignABOUT THE AUTHORSAssoc. Prof. Tsvetozar Georgiev, PhD, Department of Computing, University ofRousse, Phone: +359 82 888 827, -mail: firstname.lastname@example.com Georgieva, MSc, Department of Computing, University of Rousse,Phone: +359 82 888 577, E-mail: firstname.lastname@example.com
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INTRODUCTIONVirtual or On-line computer labs are a means of offering a similar experience tothat of physical computer labs, but allowing students to perform most of the labsactivities from almost everywhere, even at home. A virtual lab is a broad concept,whose main advantage is that it allows student access to complex equipment orsoftware that cannot be easily distributed. On-Lab [6] is a tool which offers a webenvironment, where interfaces can be quickly and easily developed for physical orvirtual lab instruments and on-line exercises can be created based on thoseinstruments.Virtual computer labs are becoming a vital part of teaching computer science usingblended learning [1,3,5]. Blended learning [2,4] is an emerging trend in education andcan be considered the first step towards achieving a full e-learning experience. TheAndalusian Virtual Campus2 (AVC) is an example of how Andalusian universities aremoving towards e-learning. In this virtual campus, ten universities share a commonspace where some courses are officially offered by e-learning.As mentioned before, one of the main difficulties that has to be faced in this kind ofexperience is the remote use of the required software and hardware resources. Beingable to offer virtual labs helps us to: Reduce the number of software licenses Save installation time and money Improve accessibility to the software. Increase the availability of the labs to 24/7.PROPOSALWe have created an infrastructure of virtual computers based on classic webtechnologies that allow students to connect to a computer inside the University at anytime and from anyplace, without having to install the centralized software in their owncomputers.This virtual infrastructure provides the following advantages:
A central server stores all the virtual computers. This way fewer physicalcomputers are needed and existing ones will be used more efficiently (formanagement of the global storage capacity, percentage of CPU load, reusing oldsoftware licenses, clusters, etc.). This translates into a meaningful reduction ofthe hardware cost, its maintenance and the space required for storing it.
2 http://www.campusandaluzvirtual.es/
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Any application installed in the virtual computers works in the usual way anddevices connected to the host computer like printers, CD-ROMs or DVD-ROMsor any other will be available to the virtual computers.
The teacher can create a virtual computer template that only includes thesoftware required by their particular students. This avoids having to coordinate allthe teachers regarding specific software versions, etc.
Each student is assigned their own virtual computer in which they can work ontheir exercises and homework.
The changes that the students make to their virtual computers will not affect thehost computer.
If a virtual computer crashes this will not have any effect on the rest of the virtualcomputers or even the host computer.
The other existing resources in the labs must be available to be accessed remotelywhen the computer labs are closed, at night or at weekends..There are several different schemes available for virtualization depending on thepurpose of the virtual systems. Below, we propose one such scheme that easily allowsthe implementation of a virtual computer lab.At least a server is required that stores and runs all the virtual computers, as wecan see in Figure 1. Depending on the number of virtual computers necessary, we mayrequire more than one server to run and store them.Apart from the server that stores and runs the virtual computers, the role of thebroker that offers the interconnection of the virtual computers and the physicalcomputers in the classical labs is vitally important.
After searching the market for computers that fulfilled these requirements,validated by VMWare, we found the HP Proliant 380 DL server, which had a good PriceQuality ratio. The cost of this server is approximately 3.000, not including- 153 -
e-Learning09maintenance. The ideal hardware configuration will employ two servers runningconcurrently, ensuring greater fault tolerance and capacity.SoftwareIn order to implement the system we also need the following software VMwareESX and VMware Virtual Center, as explained previously.A rough estimation of the costs for a VMware ESX license and a Virtual Center foraround 40 virtual computers including Gold Support is 7.000 VAT included3Having two servers requires an additional licence for VMware Infrastructure, (VIENT-A y VI-ENT-G-SSS-A), although only one Virtual Center license is needed in orderto manage these two servers.As mentioned in the introduction, in order to manage the virtual computers weneed an application that plays the role of a broker. The cost of the Sun&Seeds broker isapprox. 50 (VAT included) for each virtual computer when the number of total licensesis less than 60, which is our case. Thus, the estimated cost for our architecture usingthe Sun&seeds Broker is almost 5000, taxes included.Putting together the hardware and software cost makes a total of approximately15.000 which is roughly half the price of setting up a traditional computer lab at a priceof 400 per computer.PerformanceThis system had been used in the last academic course, but only in the AVCsubjects. The theoretical configuration says a good performance can achieve usingapproximately 8 virtual machines per CPU core. Then, for a server that has two quadcore processor the theoretical limit of virtual machines running simultaneously would be64.We have checked that in practice, with this processor the real limit was approximatelyon 35 concurrent virtual machines, although it depends on what kind of applications thestudents are running in each of the virtual machines. For this particular estimation thestudents were using the software Mathematica, from Wolfram. We have had an averageof 30 different virtual machines running per day, and a maximum of 10 concurrent virtualmachines.CONCLUSIONS AND FUTURE WORKE-learning requires new resources to achieve a collaborative environment whereprofessors and students can interact. When these resources are software applicationswe should be able to use them regardless of the final user operating system or the othersoftware installed. Nevertheless, the user cannot install these applications when acommercial license is required, so a way to provide a remote access to suchapplications must be made available.The best way to guarantee that users can avail of these applications is to provideremote access to a virtual machine. Each virtual machine is independent and it can becopied many times. Each one has its own operating system and software applications.An important part of the infrastructure is the broker. This is the web application thatprovides user authentication and virtual desktop access. It is interesting to note that theresulting architecture is not only more flexible than a traditional lab but it is also lessexpensive and easier to manage.In the Andalusian Virtual Campus a VMware solution has been adopted. Thisapproach offers an end-to-end solution called VMware View (formerly VMware Virtual3 The VAT rate in Spain is 16%
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e-Learning09Desktop Infrastructure (VDI)) that organizations use to provide remote users withaccess to virtual computers that are hosted in a central datacenter.As future work we are developing a federated identity so that every federated usercan be authenticated in a virtual machine and depending on the user attributes, they willbe able to access a specific virtual machine.REFERENCES[1] Jianjun Xie, Junhong Li, Yi Geng, "Blended Learning in Computer HardwareCourse Education," csse, vol. 5, pp.905-908, 2008 International Conference onComputer Science and Software Engineering, 2008.[2] R., & Allen, S. (2005). Blended learning: An emerging trend in education. In C.Howard, J. V. Boettecher, L. Justice, K. D. Schenk, P. L. Rogers, & G. A. Berg (Eds.),Encyclopedia of distance learning (pp. 172179). Hershey, PA: Idea Group.[3] Fu Wang, Joseph Fong, Marian Choy, Tak-Lam Wong, "Blended Teaching andLearning of Computer Programming'' Advances in Web Based Learning ICWL 2007(2008), pp. 606-617.[4] C.R. Graham, Blended learning systems: definitions, current trends, and futuredirections, Handbook of Blended Learning: Global Perspectives, Local Designs. Bonkand Graham (eds), Pfeiffer Publishing, San Francisco, CA, 2006, pp. 321.[5] Abbas El-Zein, Tim Langrish and Nigel Balaam. 2009. Blended teaching andlearning of computer programming skills in engineering curricula. Advances inEngineering Education. Winter 2009 Volume 1 Number 3[6] Marco Alfano, Biagio Lenzitti , Rocco Versace, "On-Lab: A web environment forOn-line Labs development" International Conference on Computer Systems andTechnologies - CompSysTech 2005ABOUT THE AUTHORSAssoc. Prof. Juan J. Ortega, PhD, Department of Computer Science, University ofMalaga,Isaac Agudo, PhD, Department of Computer Science, University of Malaga
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INTRODUCTIONThe term e-Learning nowadays is being herd more and more frequently. Thereason for this is the dynamic of the life and the new demands for the modern human.Laboratory experiments are a vital part of engineering education, which have beenconsidered for a long time as impractical for distance and e-learning learning. However,recent advances in Internet/web technologies and computer-controlled instrumentationpresently permit net-based techniques to be utilized for setting up remote laboratoryaccess [1].The trends for development of the modern technologies require more flexibleand continuous learning. This is a process that starts with the human birth and it is alifelong. Thus we talk often about life-long learning. The both phenomenon e-learningand life-long learning are demanding new approaches and technologies for conductingand supporting the learning process. This process should be adapted for the daily roundtasks and it should not disturb the routine business and personal engagements. Thatswhy the requirements for this process are quite complex. The modern learningtechnology should allow usage, independent from the place and time. The instrumentsproviding it should be flexible, scalable, intuitive and able to give a touch to modernand expensive equipment.THE PROBLEMTo provide the touch to learning equipment for the Embedded Systems course,in the Department of computing in the University of Ruse, we decided to use a systemfor remote access and control. Such systems exist and are used from a couple of years.One of the popular is Nokias RDA (Remote device access) [2], which allows access totheir symbian based phones to programmers, testers, enthusiasts, or just curiousbuyers, who would like to play with the device and to evaluate it before buying. Thisapproach is quite useful, since for a software developer it will be quite expensive, if hehas to own all of the devices that could run his application and to test it.Another solution for remote access is the LabView suite from National Instruments[3]. It provides remote access to Nationals products and is quite popular for internetaccess to the labs [4].On one hand there are existing systems that allow remote access, but there aretwo main problems for their usage: The first one is the price since we talk about department of ComputerSystems and Technologies, it is more appropriate such system to bedeveloped by its staff and students, rather than spending funds for it; The second factor is the universality usually these systems are quitespecific and they work only for a given scenario and hardware equipment. Inthis meaning they are custom systems and the cost of ownership includesalso a kind of a customization, which can vary from just a slight cosmeticadaptation to re-engineering of the product- 156 -
e-Learning09THE SCENARIOFor the course Embedded Systems, we are using hardwaredevelopment/evaluation boards, based on MCS-51 MCUs. The students can accessthem only during the normal classes. This way they can not experiment their ownprojects, ideas, or just experimental analyze the details of the microprocessor family. Onanother hand, the teachers could not place assignments, related with the developmentboards without such a system. Besides it should allow access control and priorities forthe different groups of students. Obviously the students that have assignments shouldbe in the highest priority list. The ones that attend the course are in the lower and therest of them are in the lowest priority list.The next question we have to answer is the technology of access and feedback.The two possible variants for our case are: Feedback via software kernel, which allows program execution, using timedivision and communication via RS232. This could be a nice solution if wehad only on kind development boards. Having different boards, withdifferent MCUs require different versions of the software kernel, which isquite time consuming, expensive and not so flexible. Using video feedback is a much simpler solution, which allows the usage ofdifferent development boards and peripheral devises with minimal costs andefforts. The system is web based and could be easily integrated with theexisting e-Learning platform. Actually, it could be built as a plug-in modulefor the e-Learning system. The additional software, needed in this case is aprogram loader (usually free, downloaded from the manufacturer) and avideo streamer and client (in our case Adobe Flash based, which is alsofree and open source)Figures 1 and 2 represent the main stages of the process of scenario developmentand application building.2Process startUser interfacedevelopmentDefining theproblems forsolving
Analysis of theinterfacefunctionality
Interface correction
Scenario analysis
Scenario correction
Application coding
The scenariofits the tasks
Fig.1- 157 -
Fig.2THE ALGORITHMThe aim for the algorithm realization was to keep it as simple as possible. Thestages are: Authentication here the user identifies him to the system. We use the logindatabase from the existing e-Learning Shell, which saves time and it is notnecessary to create new users; Checking for an available device since the devices are limited, and thestudents are generally more than the available development boards. It is possiblea student to attempt access, when all boards are busy; Suggest a time slot if all boards are busy, then suggest a time, after which thestudent will get access to the device; Slot reservation the user may accept, or deny the use of the suggested slot.This may happen if the waiting time is too big. In case of deny the systemfinalizes the session; Grant access to the device at this stage is the real work of the student with theboard; Free the device after slot expiration after given amount of time, the deviceaccess should be granted to another userThe described stages are illustrated on figure 3.
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e-Learning092User name andpassword entry
Valid data
YESWaiting for the slot
Grant acess todevice
NOSuggest time slot
Session end
Fig.3.CONCLUSIONS AND FUTURE WORKThe realization of the remote access system should improve the usability of thelaboratory devices and should motivate the student of hardware course to spend moretime on development and research. This technology should ease their access todevelopment equipment.Next step on this project is to test the system in real conditions and to define thelength of the time slot. It should be not too long in order to keep the time for waiting asshort as possible. After this step is done, the system should be integrated with the eLearning Shell system as a module and priority queues should be organized, for at leastthree levels: First level priority the priority level for students with assignments; Second level priority for students, currently attending the course; Third level priority for all other users of the system.In parallel with the prioritization, we should develop also a user interface for theteachers to manage student accounts in these 3 priority groups.
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e-Learning09REFERENCES[1] Shen, H., Xu, Z., Dalager, B., Kristiansen, V., Strm, ., Shur, M. S., Fjeldly,T. A., Lu, J.,Ytterdal, T., "Conducting Laboratory Experiments over the Internet", IEEETrans. on Education, Vol. 42, No. 3, 1999, pp. 180-185.[2] http://wiki.forum.nokia.com/index.php/RDA[3] http://www.ni.com/labview/[4] Smith, K.., Strandman, J. O., Berntzen, R., Fjeldly, T. A., Shur, M. S., Shen, H.,"Advanced Internet Technology in Laboratory Modules for Distance Learning", Proc.ASEE 2001 Annual Meeting, Albuquerque, NM, accepted for publication.ABOUT THE AUTHORMSc. eng. Orlin Tomov, Department of Computer Systems, University of Rousse,Phone: +359 82 888 276, -mail: email@example.com.
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INTRODUCTIONIn this paper, we describe our experience with teaching software processdevelopment using computer-based approach. Problems in teaching SoftwareEngineering discipline consist of keeping students motivated and keeping them focusedon general ideas instead of details.Most of the typical undergraduate software engineering (SE) books [1-5]accentuate on the theory of software development (process, requirements, design,testing, and project management) but do not discuss enough the real softwaredevelopment practice (few pages in the books actually discuss use cases) or discusspractices that can be applied only to large projects. Methods for teams of less than tendevelopers are not enough discussed in the literature; yet these methods could helpnew engineers succeed with their first projects.As a result of the dominating survey-style approach of the above textbooks, thestudents lose motivation and find them irksome. We can claim that students learn bestwhen they learn something they can understand and really apply. Based on our morethan twenty years practice in Computer Sciences and Engineering Department of theTechnical University in Varna we are persuaded that one of the main goals during eachsoftware engineering teaching course consists of practice students how to use a coupleof techniques and tools for real software projects development.TEACHING SOFTWARE ENGINEERING COMPUTER-BASED APPROCHThe teaching packageOur goals during the last two years of software engineering teaching were to helpstudents develop good software development habits, and to encourage them to seesoftware development as a systematic and computer-aided discipline. Developing andusing a package of different tools during the labs we tried to ensure the students thatthe tools are very important, useful and effective in the real software developmentpractice. We hope we have found an effective teaching approach that we would like topresent in this paper.Our software development teaching model (figure 1) during the last couple of yearsaccentuated on the applied side of software engineering teaching, using a package oftools, instead of trying to instruct students in theory. An opened for later extensionpackage for laboratory exercises and self study has been developed for this purpose.To the moment it consists of the following four tools: A tool for Software Project Management (PMS), presented in [6].Project management software (PMS) is a term covering many types of software,including scheduling, cost control and budget management, resource allocation,collaboration software, communication, quality management and documentation oradministration systems, which are used to deal with the complexity of large projects.PMS that we have developed can be determined as native web-based and multi-project- 161 -
e-Learning09enabled PMS. Each user can work on more than one project and on more than onetask. The projects have specific starting and completion dates that can be controlled bythe project leader and the user himself. A tool (VLT) with lecture notes, exercises and tests in Microsoft.Netprogramming.VLT is a case of web-based laboratory educational software that aims at helpingthe student study the relevant program language. Before doing the current laboratoryexercises, the students could read the language materials. Choosing Tests pane thestudents could make the corresponding quiz and evaluate their learning progress (seefigure 2). The teacher also can analyse the students' results, and get an overall view forthe progress of the students. The architecture of the developed web-based tool providesinstructors a possibility to easily create and manage different course materials.Nevertheless the tool doesnt impose limitation about the program language materialsthe students are suggested to use a .Net language.Teaching packageSCE
Implementation & Testing
Operation &Maintenance
The tool (see figure 3) is based on the widely used Basic COCOMO andCOCOMO II models [1-5] for software cost estimation. Software estimation is the part ofproject planning devoted to estimating the size, effort, time, people required etc.Software estimation process gives the information needed to develop a softwareprojects schedule, budget and assignment of personnel and resources. Estimatingproduct size is the basis of software estimation. The following three measures aregenerally used for software size estimation in the developed tool: Source Lines of Code(SLOC), Function Points and Object Points. Using size as input, estimates of effortapplied, development time and people required can be made. Below the basicCOCOMO equations are presented:Effort Applied = ab(KLOC)bb [man-months], where KLOC=SLOC / 1000.Development Time = cb(Effort Applied)db [months]People required = Effort Applied / Development Time [count]The coefficients ab, bb, cb and db depend of the mode of the project (organic, semi- 162 -
e-Learning09detached or embedded) and are 2.43.6 for ab, 1.051.20 for bb, 2.5 for cb and0.380.32 for db. According to Boehm in organic mode, relatively small software teamsdevelop software in a highly familiar, in-house environment. The semidetached mode ofsoftware development represents an intermediate stage between the organic andembedded modes. In embedded-mode the software teams develop software respectinga complex of strongly constraints, such as hardware, software platform, etc. A tool for Software Re-structuring and Analysis (SR&A) is integrated in thepackage [7].Using this tool the students can specify, document, analyze and re-structure thesoftware structure. During the specification stage the students specify the softwarestructure as a weighted oriented graph G=(X, U), where X (N=|X|) is the set ofnumbered nodes that models the set of components (classes, modules, files, packages,etc.) of the software system and U is the set of edges that represent the dependencies(e.g., procedural invocation, variable access, etc.) between the components of thesoftware system. The weight wi of each node xiX, i=1N could be interpreted as thenumber of the components elements (for example as the number componentsfunctions) or as the importance (rank) of the corresponding component. Next, thestudents choose the appropriate re-structuring analysis algorithm, based on ClusterAnalysis method, Formal Concept Analysis (FCA) method or Program Slicingtechniques. A set of heuristic algorithms corresponding to the cited analysis methodsare available: a descent hill-climbing algorithm, a Tabu-Search algorithm, a FCA based algorithm and a static slicing algorithm extracting components of the program(slices) that effect on the values of some variables (the slicing criterion). The lastalgorithm is based on well known Depth-first search (DFS) algorithm for traversing thegraph G. The first three algorithms treat re-structuring as an optimization problem: eachalgorithm tries to find a good partition of the graph G, minimizing a goal function k (1)and satisfying the constraint W0 (2) related with the weight Wd (Wd is the sum of theweights of all nodes in cluster Xd) of each cluster Xd, (d=1M, Xd=X) produced.Changing W0, the students can change the number of the clusters and can guide thealgorithm to converge to a different solution.The tool provides visualization of the initial software structure and the re-structuredsolution (the final solution). In order to find a better solution this feature helps thestudents to make the appropriate changes to the initial software structure.
(1 )
k =
2 i =1 .. M
k ,i j
j = 1 .. M
Wd = w i <= Wxi X
The goal function k (1) is sum of the number of edges between the clusters in apartition found. It is a measure of the quality of the partition. The good partitionminimizes its value. M is the number of clusters in the given partition; kij is the numberof edges between each couple of clusters.The teaching experienceDuring the first five weeks of each usually fifteen weeks term the students createdteams and started on learning the needed environment (Visual Studio.Net) and theprogramming .Net language using the integrated in the package tool (VLT) with lecturenotes, exercises and tests (see figure 2).During the next two weeks the students started on developing their term project inteams. They specified, approved and estimated the software project with the aid of- 163 -
e-Learning09SR&A and SCE tools. The team leader shared the different tasks between the teammembers.During the next six-seven weeks the team members worked on coding their termproject. They demonstrated the realized .Net solution in the last week of the semesterand presented the created specifications and user documentation. The team leader wasresponsible for the overall team work. Using PMS the team leader effectively managedthe project works.CONCLUSIONS AND FUTURE WORKOur experience with teaching software process development using computerbased approach is commented in this paper. Problems in teaching SoftwareEngineering are maintaining enough self-discipline and finding proper tool support.The paper presents an effective computer-based approach for SoftwareEngineering teaching. An extensible package of different tools has been developed forthis teaching course, its model is described in figure 1 and our experience with its usageis briefly commented. Our observation is that the effect on the rate of student learning ispositive. The number of students choosing term projects and graduation thesis, basedon .Net Framework has increased nearly twice. It is evident that as a result of thisapproach the students will be able to work as part of a software development teamwhen they graduate. In the last decade a lot of foreign software companies haveentered our country that needs skilled professionals. The number of graduated studentsworking in leading industrial companies and developing high quality code inMicrosoft.NET programming environment increases significantly. Using the computerbased approach, the students obtain very quickly the following software engineeringskills:
Use Software cost estimation tools during the early software developmentphases (like SCE).Create and adjust the specifications using the software re-structuring tool(SR&A). The student learn that when the specifications change (as a resultof the change of the requirements), the specifications must respond to thechanges.The students learn just enough the graph theory to make describe thesystem's structure and to use efficiently the software re-structuring tool.The students must also provide user documentation. The createdspecifications can be used as documentation (for future students tounderstand the project).Create teams (not more than fifteen students) and work as a member of ateam.Each team is assigned a project leader by the team itself. The team leadermanages the overall software development work using PMS. The teams arefree to use their own work style.The teams might develop software iteratively and incrementally using a newsoftware language and environment (VLT). At the end of the iteration theteams might demonstrate their work.
Further work is needed in order to extend our teaching package. The package canbe applied in other Software development courses as well. Our idea is to develop andintegrate in the package a tool for software testing. Software Testing is a costly stage(around 50% of the labor expended to produce a working program) of the software lifecycle that is usually performed by an independent group of testers after the functionalityis developed (before the software is shipped to the customer). Test techniques (unitlevel test, integrated level tests and system level tests) are not limited to the process of- 164 -
e-Learning09executing a program unit or program system with the intent of finding software bugs.Software testing techniques (black-box or | and white-box) for validating and verifyingthat the software unit or system meets all planned functional and user requirementscould be implemented by the suggested testing tool.
Figure 2: VLT a tool with lecture notes, exercises and tests in Visual.Net programming
Figure 3: SCE - tool for Software Cost Estimation (the main form)- 165 -
e-Learning09REFERENCES[1] Eric J. Braude, Software Engineering: An Object-Oriented Perspective. JohnWiley 2001.[2] Leszek A. Maciaszek and Bruc Lee Liong, Practical Software Engineering: ACase Study Approach, Addison-Wesley 2005.[3] Bernd Bruegge and Allen H. Dutoit, Object-Oriented Software Engineering.Prentice Hall, 2000.[4] Shari Lawrence Pfleeger and Joanne M. Atlee, Software Engineering: Theoryand Practice, Third Edition, Prentice Hall 2006.[5] Roger S. Pressman, Software Engineering: A Practitioner's Approach, SixthEdition. McGraw-Hill, 2005.[6] Violeta Bozhikova, Mariana Stoeva, Krasimir Tsonev, A practical approach forsoftware project management, CompSysTech Conference 2009, Russe (to appear)[7] V.Bozhikova, M. Stoeva, A. Antonov, V. Nikolov, Software Re-structuring (Anarchitecture-Based Tool), ICSOFT 2008, Third Intl Conference on Software and dataTechnologies, pp.269-273, Porto, Portugal, 2008.ABOUT THE AUTHORSAssist. Prof. Violeta Bozhikova, PhD, Department of Computer Sciences andEngineering, Technical University of Varna, Phone: (+359) 898 279 887, -mail:firstname.lastname@example.com. Prof. Nadezhda Ruskova PhD, Department of Computer Sciences andEngineering, Technical University of Varna, Phone: (+359)886 770 125, -mail:firstname.lastname@example.com.
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INTRODUCTIONWhen integrating applications, ontologies often play a substantial role in logicalunderstanding of the problem domain [6]. Ontologies have for long been used assemantic models for structured presentation of concepts and their relations that facilitatethe exchange of knowledge between humans and machines. Accepting ontologies assuch helps solving the information integration problem by turning different process anddata models into views that are understandable to both end-users and applicationintegration designers. Proper understanding of application semantics is, therefore, acommon prerequisite for successful application integration.In science, Ontologies have been used for decades to systematize scientificinformation in the form of a common vocabulary. Ontologies as such can be regardedas supportive means that facilitate reasoning and information analysis for a givenproblem domain. Therefore, they are mostly used for formally representing domainknowledge. They typically contain objects, concepts, properties and relations betweenobjects. Moreover, ontologies can serve as useful generalizations that make theproblem domain more comprehensible to the concerned stakeholders.In this paper we address the following question: can we use existing semiautomatic ontology construction tools to speed up the traditionally manual process ofapplication integration. Traditional way of manual construction is time consuming andtypically requires several phases to complete. As a case study for the applicationintegration using ontology as metadata we took the National Housing Savings Schema(NHSS) domain, which has some typical characteristics that justify the implementationof e-government integration principles [1]. First, NHSS is a state project governed by thelegal Act. Second, there are several actors involved, including the Ministry, the HousingFund, several business banks, and citizens. And third, NHSS offers an incentivefinancial instrument to help citizens acquire suitable housing facilities. Therefore, thecitizens that are saving their money in NHSS are entitled to gain access to exact andup-to-date information about the status of their savings, while the other threecooperating actors have to provide services for accessing such information. Althoughproviding such services to citizens seem relatively simple at first sight, various roles ofthe involved actors are causing increasing complexity that calls for an efficientimplementation of integration and interoperability principles [1].The main purpose of this article is to provide information technology viewpoint onapplication integration based on constructed ontologies. We primarily focus onintegration and interoperability issues related to exchanging information between thegovernment institutions and business banks included in NHSS project with a goal tospeed up and add value to the business process. First, we describe the context and theunderlying business process. Then, we elaborate on some of the most crucial phases ofapplication integration with other actors in the process, discussing the issues related to- 169 -
e-Learning09the information technology. Next, we present a few topics concerning the actualimplementation of supporting the underlying business process. Finally, we conclude bypointing out the most important findings in the paper.RELATED WORKTraditionally, ontology construction is a manual task that consists of determininginteresting domain concepts and establishing a hierarchy of such concepts. Theprocess uses a special sort of description language, in which common domainknowledge is represented. In the last decade several computer programs have beendeveloped that support and speedup such manual ontology construction, for exampleProtg [4]. To demonstrate the programs ability we took the legal act governing theNHSS domain as a case to construct the domain ontology. For the task we employedan expert from the field and provide her with the support of using Protg shown infigure 1. The concepts from the ontology typically form the core structure for exchanginginformation in application integration with other actors. This step is crucial in achievingconceptual understanding necessary for successful cooperation between theresponsible developer teams.
e-Learning09Developing ontologies helped all of the involved parties to easily locate and interpretimportant information [7].LEARNING ONTOLOGY FOR SEMANTIC APPLICATION INTEGRATIONThe quantity of knowledge stored in the form of scientific publications, articles andbooks grows exponentially in the recent years. Consequently, the interest in using textmining technologies evidently rises, primarily because effectively following the progressin large quantity of published knowledge is almost impossible even in a relatively narrowfield of interest. Written text typically contains large quantity of information that is usuallyencoded in a way hard to comprehend for automatic approaches. In spite of that fact,information technology spawned, besides the area of text mining, several importantapplication areas like information retrieval, computational linguistics, text categorization,ontology learning and hypothesis formation.With the emergence of new text mining technologies ontologies can beconstructed semi-automatically by processing available text documents [e.g. 2]. Sincemanual ontology construction is a complex and intricate process that requires both skilland diligence, the need for developing more active and helpful computerized support isevident. In the last decade several approaches for facilitating semi-automaticconstruction of ontologies have been developed and successfully used in practice,making the process of ontology construction more effective and viable. One example ofa tool for interactive construction of ontologies from text documents is OntoGen [3],which has already been proven successful in several real-world applications. A user canform concepts, edit them thematically and assign documents to the formed concepts. Byimplementing modern machine learning techniques OntoGen can help users in allcrucial phases of ontology construction, suggesting concepts and their names andautomatically assigning documents to the proposed concepts [3].An example of ontology constructed with OntoGen on the legal act covering theNHSS is presented in Figure 2. The constructed top-level ontology gives an insight intothe structure of the studied domain; it is, therefore, particularly useful in the process ofobtaining initial acquaintance with the domain. Note that it provides some sort of birdseye view on the domain under study. It is worth mentioning that the constructedontology was meaningful and correct to the legal expert from the NHSS field; besides,the top-level structure is very similar to the manually constructed ontology in Protgfrom figure 1.Besides the mentioned activities the Housing Fund managed to improve also thetransparency of the NHSS business process. There are three different actors involved inthis process: the Fund, business banks and citizens. The Funds role is central, since itcarries out the process and it is responsible for direct communication with the other twoactors. One of the banks is engaged in all eight public tenders and concludes almosthalf of all saving contracts. This banks group is one of the largest state internationalfinance groups with 1.500 correspondence connections in 140 states. Requirements forsaving in the NHSS before year 2006 were very strict and savers had to pay theirmonthly fee strictly within certain month. In case they forgot payment for one month ithad to me carried in next one such as current one. In case saver had forgotten hispayment more than once in whole period of saving, he lost all benefits such aspremiums and ability to get long-term loan. To avoid this unpleasant situation, the Fundimproved communication with savers in a way saver can look at situation on his savingcontract in every moment. This is possible with link, which is put on the Funds webpage and updated monthly. Only thing the saver need is the username and password.Last year the Fund and the bank implemented a step further by employing Web Servicetechnology to enable interoperability of their information systems. This enables saversto see updated situation on their saving contracts.- 171 -
Fig. 2: Semi-automatically constructed ontology with OntoGen from the underlying legalact.By definition, interoperability is the ability of two or more systems, or componentsto exchange information and to use the information that has been exchanged. Withrespect to software, the term interoperability is used to describe the capability ofdifferent programs to exchange data via a common set of business procedures, and toread and write the same file formats and use the same protocols. In IT we candistinguish between several types of interoperability that can be ranked hierarchicallyfrom application interoperability, through semantic and enterprise interoperability toenvironment interoperability. Most of the up-to-date e-Government interoperablesolutions seem to function solely on the application level; moreover, even on this levelthey function in a rather fragmented manner. However, we believe that one of the mostimportant challenges in the next decade will be shifting the interoperability paradigm tothe level of semantic and enterprise interoperability, which will, among other things, inthe long run rely heavily on already developed semantic web, metadata and knowledgemanagement technologies.The Fund and the banks carry on the majority of interdependent work to supportthe NHSS business process implementation. At first sight banks could deal with the taskalone since it mostly falls in the category of their regular business. However, after eachcompleted year the savers are entitled to receive a state premium accrual if they hadpaid their instalments regularly and according to the established plan. Since the Fund isresponsible for validating savers compliance to the plan, it needs to acquire data aboutthe contracts and paid instalments. To actively keep an eye on the things, the Fundissued a protocol that governs the necessary data exchange between the banks and theFund. The quantity of the exchanged data is relatively high; however, the frequency isquite moderate since the exchange has to take place only once or twice every month.The exchange protocol is robust enough, since it relies on the ontology build for NHSSdomain. The ontology is considered as a basis for completely automated interoperabilitysolution using message queuing technology.Since the Fund decided to maintain its own database for monitoring savingaccounts, it was a natural move to make it available to interested banks and citizens- 172 -
e-Learning09through a web application. In such way the banks are able to use the Funds databasefor advising citizens and to proactively handle potential problems like missinginstalments etc. The web application takes into account the sensitivity of the data so itallow secure internet access HTTPS for both, banks and citizens. Here, the citizens canobserve the status of their saving account, which gives them additional sense of controlover the saving maters. The introduction of the Funds G2C service had also quitesurprising side effect: several banks decided to offer additional B2C service to the samecitizens, thereby narrowing the time and information gap by providing even moreaccurate data on the Internet.After establishing NHSS project, the Fund issued detailed requirements for dataexchange with the corresponding banks (Figure 3). These requirements are based onthe constructed ontology; thorough understanding of application semantics is a commonprerequisite for successful application integration. At first, the amount of data wasrelatively small and the exchange was technically carried out using simple storagemedia as well as the Internet services. Here, data protection and privacy issues had tobe considered as well.
e-Learning09process, the value added to the main an sub-processes is that the processmanagement is even easier and more effective. As a result we can notably speed-upthe process of getting acquainted with a given problem domain, mostly because thegenerated birds-eye view quickly reveals the most important top-level domain concepts.We demonstrated that the resulting ontology could serve as a firm basis for a commonunderstanding between several stakeholders as well as for integrating otherapplications in an interoperable manner.The constructed domain ontology can be used to support both, process view anddata view [5]. The process view represents information system dynamics and is,therefore, crucial for establishing communication protocols for application integration.Common understanding of underlying business processes is crucial for establishingresponsibility matrix for handling events generated by the integrated applications. Onthe other hand, data view incorporates static structure for exchanging information. As aconsequence, it can serve as a backbone in the process of designing the integration.Semantic application integration can be used to achieve applicationinteroperability; therefore, modern technologies like Web services and Service OrientedArchitecture have to be considered in the process. Moreover, the new trend ofsupporting ontologies as underlying data models for machine data interpretation plays asubstantial role in the next generation of Web services Semantic Web services. It isour firm belief that this kind of built-in knowledge will lead to a significant time andmoney savings in further development, deployment and integration of applications.REFERENCES[1] Cestnik, B., A. Kern. IT for supporting the Slovenian National Housing SavingsSchema: example in G2B interoperability. 5th Eastern European e|Gov days, Prague,April 2007.[2] Cestnik B., A. Kern, H. Modrijan. Semi-automatic Ontology Construction forImproving Comprehension of Legal Documents. Electronic Government, 7thInternational Conference, EGOV 2008, Turin, Italy, August 31 - September 5, 2008.Proceedings, Lecture Notes in Computer Science, Springer, pp. 328-339.[3] Fortuna B., M. Grobelnik M., D. Mladeni. System for semi-automatic ontologyconstruction. Demo at ESWC 2006. Budva, rna Gora, June, 2006.[4] Gennari J., M.A. Musen, R.W. Fergerson, W.E. Grosso, M. Crubezy, H.Eriksson, N.F. Noy, S.W. Tu. The Evolution of Protg: An Environment for KnowledgeBased Systems Development. 2002.[5] Kroll, P., P. Kruchten. The Rational Unified Process Made Easy, APractitioners Guide to the RUP. Addison-Wesley. 2003.[6] Linthicum D.S. Next Generation Application Integration, From SimpleInformation to Web Services. Addison-Wesley. 2004.[7] Sagev A., A. Gal. Putting Things in Context: A Topological Approach toMapping Contexts to Ontologies, Journal on Data Semantics IX, pp. 113-140. 2007.ABOUT THE AUTHORSAssoc.Prof. Bojan Cestnik, PhD, Temida d.o.o., Ljubljana, Slovenia, Phone: +3861236 3350, -mail: firstname.lastname@example.com Koti, MSc, Temida d.o.o., Ljubljana, Slovenia, Phone: +386 1236 3350, mail: email@example.com.
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e-Learning09thesis, that knowledge potentials of knowledge nodes reduce or lose their level or valueagainst other knowledge nodes if they do not develop and extend their knowledge.Therefore, every knowledge node tries to open and make accessible new knowledgesources. The development of knowledge takes place on the one hand by the exchangeof knowledge within the knowledge node (as far as it consists of more than oneindividual) and the exploitation of explicit knowledge of external sources. On the otherhand, there is the need for compensating and exchanging the knowledge potentials withother knowledge nodes (Figure 2).
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e-Learning09THE IMPLEMENTATION OF KNOWLEDGE FLOWS IN EDUCATIONALNETWORKSFor implementing the described knowledge flows in networks it is necessary tomanage the transformation of the classic monolithic learning systems to new forms ofopen and dynamic networking. Therefore, a meta modular-design model for educationaloffers with a dynamic structuring has to be built up. The information volumes of basicteaching, applied uses of economy and outcomes of research works will be centralizedin knowledge blocs (Figure 4).
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e-Learning09services for the end users and for improving the individual knowledge and competencetransfer.These main levels form a special three level architecture for imaging aneducational knowledge transfer (Figure 7).The specific architecture of educational knowledge transfer can be the next levelfor an application in a learning mobile environment.THE EXPANSION FOR MOBILE LEARNING IN VOCATIONAL TRAININGNETWORKSMobility and local independence are one part of mobile learning (m-learning) in thecomplexity of e-learning. Fast accesses as well as current interruptions of learning flowsare thereby further challenges a mobile learning environment has to be adjusted to.These aspects require for methodical, didactical and multimedia processing of learningcontents.Apart from these requirements there is as well a high technological dependence inthe field of hardware. Wireless broadband connections and the use of mobile terminalsmay prevent a successful conception of a mobile learning environment. However thesespecial technologies may improve the knowledge of using e-learning since the spatialexpansion of a mobile learning environment is enormously increased in contrast to apurely stationary environment.
e-Learning09In the surroundings of a professional training the m-learning can be used for thesupport by vocational training processes. In this case above all, the spontaneous,individual use plays still another basic role, whereby the study content/course is usuallyretrieved to close knowledge gaps. Newer investigations and concepts in the field of aprofessional training try to support working and networking of study groups in virtualclassrooms with the help of m-learning [10][11]. In this process above all, thecooperative and self-organized learning as well as the connection of learning andknowledge management should be strengthened. Here the inclusion of the Web 2.0 technologies and social software, which show high potential in other fields ofcooperative and organized communication and information retrieval, appearsparticularly important.The solution of these complex challenges for an optimal conversion in theprofessional training is, like in other subject areas also difficult, and usually lengthily. Mlearning thereby can only be seen as completion to other teaching methods and scenarios. But already the possibilities, which a mobile learning environment provides,should be exhausted, to raise/increase the motivation of the trainee/apprentice as wellas the quality within the apprenticeship.CONCLUSIONS AND FUTURE WORKThis paper shows the diverse levels of specialisation of knowledge flows. The firstlevel contains the knowledge model with knowledge nodes and knowledge flows. Thismodel is essential for the organizational learning level, because knowledge is the elearning content. The organizational learning will be extended in the next specializationlevel by the technical and methodic-didactic aspect, the mobile learning, for reachingmore flexibility.Therefore, the goal for the further development of these different specializedmodels is the better integration and the optimization of the processes in education,vocational training, and further training for becoming time- and place-independent.REFERENCES[1] Tittmann, C.;Schumann, C.-A. (2009). Potentials for Externalizing andMeasuring of Tacit Knowledge within Knowledge Nodes in the context of KnowledgeNetworks. In:Harorimana, D.:Cultural Implications of Knowledge Sharing,Management and Transfer. Hershey, IGI Global, 978-1-60566-790-4.[2] Schumann, C.-A.;Tittmann, C. (2007). Multilevel Cross-Linking and Offering ofOrganisational Knowledge. In: Martins, B.;Remenyi, D.: Proceedings of the 8thEuropean Conference on Knowledge Management. ECKM2007, Barcelona, 878-883.[3] Schumann, C.-A.;Tittmann, C. (2008). Agile and intelligent networks forInformation Literacy by Knowledge Sharing. In: ECKM 2008, Southampton / GB.[4] Schumann, C.-A.;Tittmann, C.;Tittmann, S. (2008). E-Education CompetenceClusters as Transfer Hubs for Methodical and Informations Literacy. In: GUIDEInternational Workshop 2008, Universitt Marconi / Italien.[5] Schumann, C.-A.; Tittmann, C.; Nske, C.; Weber, J.; Methodology andTechnology Knowledge Based Approach for E-Learning in the Future Networks ofDistance Education, Proceedings of the GUIDE 2006, Universit Telematica GuglielmoMarconi, Rom 2006[6] Schumann, C.-A.; Tittmann, C.; Nske, C.; Weber, J.; Effectiveness ofIndividual Competence Development by Using Networks of Granulated Contents, In:Szs, A. Bo, I.:E-Competences for Life, Employment and Introduction to programming Basics ofinformatics Artificial intelligence Introduction to multimedia Computer graphicsAnimation Media law Media design Media politics Innovation, Proceedings of the EDEN2006 Annual Conference, Wien 2006- 180 -
e-Learning09[7] MANDL, H. (2000). Wissens sichtbar machen Wissensmanagement mitMapping-Techniken. Hogrefe Verlag, Gttingen, ISBN 3-8017-1337-7.[8] Gerhard Zimmer: E-Learning. Handbuch fr Hochschulen und Bildungszentren.Didaktik, Organisation, Qualitt. 1. Auflage, Nrnberg: BW Bildung und Wissen Verlagund Software GmbH, 2004.[9] Maciej Kuszpa, Ewald Scherm: Mobile Learning Modetrend oder wesentlicherBestandteil des lebenslangen Lernens? Diskussionsbeitrag des FachbereichesWirtschaftswissenschaft. FernUniversitt Hagen, September 2005[10] Didaktisches Design, 2009, http://www.e-teaching.org/didaktik/[11] M-Learning, 2009, http://de.wikipedia.org/wiki/M-LearningABOUT THE AUTHORProf. Dr.-Ing. habil. Christian-Andreas Schumann, Faculty of Business andManagement Sciences, University of Applied Sciences Zwickau; Phone:+493755363418, Email: email@example.com
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INTRODUCTIONIn knowledge intensive organizations, many sources of competitiveness relymostly on intangible parts of their organization, such as the relationships of organizationwith its stakeholders, the knowledge and know-how of employees, its patents andtrademarks. Management of these resources also know as intellectual capital should enable the organization to sustain viability, success, and basis for innovation [2],[9].How exactly the knowledge resources and knowledge management processes tieto strategic, tactical, and operational business objectives and workflow is often leftimplicit or not addressed at all in business practice [6]. In order to specify theserelationships, The Knowledge governance framework developed by Smits and deMoor [2004] has been developed. A modified version is shown in Figure 1, which showsthat management can focus on the overall strategic objectives and directions set as partof governance of intellectual capital. In general new product development projects areusually proposed by technical staff, or by marketing and production staff and the focusof the projects can range from technical problems, product improvement demanded bycustomers, process improvement for production or development of new knowledge.However, usually only limited resources are available for innovative products andprocesses.Portfolio management may refer to different areas, such as investmentmanagement; IT portfolio management and project management. All these fields arevery central to enterprises or organizations knowledge management. In general,Portfolio Management is used to select a portfolio of new product development projectsto achieve the following goals: (a) Obtain value maximization; (b) Support the strategyof the enterprise; and (c) Provide balance. Portfolio management can be taken as a toolfor knowledge management in enterprises or organizations [1]. The optimal compositionof portfolio is a set of new product development projects, where capacity constrains;strategic objectives and cash flow are carefully balanced. The theoretical foundation toportfolio management was laid by Harry Markowitz [5] by starting a parametricoptimization model. With all its merits, the Markowitz model has major downsides: to geta grip of computational complexity, it has to rely on a number of rather strict technicalassumptions which are more or less from reality [4]. The limitations of the originalMarkowitz framework have stimulated a number of extended or modified methods. One- 182 -
e-Learning09of these methods is using heuristic optimization techniques. The heuristic techniquesare more general purpose search methods that do not derive the solution analyticallybut by iteratively searching and testing improved or modified solutions until someconvergence criterion is met. Since they usually outperform traditional numericalprocedures, they are well suited for empirical and computational studies.
i =1
Min wi w j ij ; sb. wi ri = R * , wi = 1, 0 wi 1 i = 1, L , N .i =1 j =1
Min wi w j ij (1 ) wi ri ; sb. wi = 1, 0 wi 1 i = 1, L , N(2)i =1 i =1
i =1 j =1
In the model included parameter , we can draw a continuous curve that is calledan efficient frontier according the Markowitz theory, the curve composed of mean returnand variance according different , and every point on an efficient frontier curveindicates an optimum, which indicates that the portfolio optimization problem is a multiobjective optimization.
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e-Learning09Instead of focusing on the mean variance efficient frontier, we seek to optimize theportfolio Sharpe Ratio ( SR ) [7]. The Sharpe ratio is quite simple and is a risk-adjustedmeasure of return, which is often used to evaluate the performance of a portfolio. It isdescribed as the following formula:SR =
Rp R f
StdDev( p )
where p is the portfolio, Rp is the mean return of the portfolio p, Rf is the testavailable rate of return of a risk-free security (i.e. T-bills), we select zero Rf in this study.StdDev(p) is the standard deviation of Rp. In other words, it is a measure of risk of theportfolio. Adjusting the portfolio weights wi. We can maximize the portfolio Sharpe Ratioin effectively balancing the trade-off between the expected return and the correspondingrisk. In this study, the PSO algorithm is used to find the most valuable portfolio withgood stock combinations.PARTICLE SWARM OPTIMIZATION (PSO)Swarm Intelligence (SI) is an agent-based intelligent paradigm for solvingoptimization problems that originally took its inspiration from the biological examples byswarming, flocking and herding phenomena in social insects. The complex behavior ofthe swarm is based on that agents follow some simple rules. PSO is one optimizationtechnique of Swarm intelligence The major feature of PSO algorithm is its simplicity inimplementation and high computational efficiency in solving optimization problems.,Researches in the filed of Swarm intelligence argue against the view that agents(individuals) are isolated information-processing entities and stresses the fact thatintelligence arises among the interaction of intelligent agents.The study of Swarm Intelligence has introduced a number of new optimizationtechniques into the field of Computational Intelligence. Kennedy and Eberhart [3]developed Particle Swarm Optimization based on the analogy of birds flocking and fishschooling. PSO has been shown to be powerful, easy to implement, andcomputationally efficient.The original PSO algorithm is thought of a picture in which K particles of apopulation flys in the D-dimensional problem space (solution space). Each particlerepresents a solution of the problem, and has a value called position of the particle,and another value called called velocity used for evolving a new position in the searchspace. The velocity of each particle is dynamically adjusted by the flying experiences ofits neighbors and its own. At each iteration, a particle moves to a new position based onthe old position and an updated velocity.How the algorithm works is briefly described as follows: 1. To initialize the size ofthe particle swarm and parameter; 2. To initialize the position and velocity for all theparticles randomly; 3. To find the global best particle in the neighbor hood based on thefitness function; 4. All the particles are then accelerated in the direction of the globalbest particle and in direction of their own best solutions that they have discoveredpreviously.Occasionally the articles will overshoot their target, exploring the search spacebeyond the current best particles. All particles have the opportunity to discover betterparticles in route, in which case the other particles will change direction and headtowards the new best particle. Since most functions have some continuity, a goodsolution will be near by these good, or better, solutions. By approaching the current bestsolution from different direction in search space, the chances are good that theseneighboring solutions will be discovered by some of the particles.- 184 -
e-Learning09The basic concept of PSO lies in accelerating each particle toward its pbest andthe gbest locations, with a random weighted acceleration at each time step. Eachparticle tries to modify its position using the following information: (1) The currentpositions; (2) The current velocities; (3) The distance between the current position andpbest; and (4) The distance between the current position and the gbest.In this paper, we apply PSO to a high-dimensional constrained optimizationproblem. That is to construct optimal risky portfolios for financial investments. A PSOsolver is developed and test on various restricted and unrestricted portfolios. The resultsof experiments demonstrates that the PSO solve has high computational efficiency inconstructing optimal risky portfolios.FITNESS FUNCTIONFitness function is a critical factor in the PSO method. The fitness function fp isdefined asN
fp =
i =1N
i i
wi w j ij
i =1 j =1
At each step, a particles position and its velocity in the swarm are updated if animprovement of the fitness values is observed.PARTICLES MOVINGEvery particle moves towards its pbest position plus the gbest position particles ofthe swarm at each one of the iterations. Indeed, this movement depends on its currentvelocity and current position. a new velocity in the problem space is shown in Eq. (5).
rrrrrrvij (t + 1) = wvij (t ) + c1r1[ pij (t ) xij (t )] + c2 r2 [ pgj (t ) xij (t )]
where index j is the dimension number of particle i, t is the iteration sequence, c1 and c2are positive constant parameters called acceleration coefficients which are responsiblefor controlling the maximum step size, r1 and r2 are random numbers between (0, 1), wis a constant, and v ij (t + 1) is particle its velocity on the jth dimension at iteration t+1.v ij (t ) is particle its velocity on the jth dimension at iteration k. x ij (t ) is particle its
position on the jth dimension at iteration k.. p ij (t ) is the historical individual best positionvof the particle. p gj (t ) is the global best position of the swarm. Finally, the new positionrof particle i, xij , is calculated by Eq. (6):
rrrxij (t + 1) = xij (t ) + vij (t + 1)
There are two types of risky portfolios [4]. One is unrestricted risky portfolios,which do not have constraints on the short selling of stocks. Investors can choose to sella stock that the investor does not own based on the condition that the investor must buyit back after a time of period, hopefully at a lower price. In other words, for unrestrictedrisky portfolios, assets could have negative weights. Another one is restricted riskyportfolios, which place constraints on the short selling of portfolios underlying equities,and require that all underlying assets must have positive weights. Both unrestrictedoptimal risky portfolios and restricted optimal risky portfolios must also satisfy anotherconstraint, i.e., the total weights of all assets must sum to 1. To construct an optimalrisky portfolio is to find the optimal combination of all assets in order to achieve themaximum Sharp Ratio. As the number of assets in the risky portfolio increases,- 185 -
e-Learning09construction of an optimal risky portfolio becomes an increasingly high-dimensionaloptimization problem with a variety of constrains.EXPERIMENTS AND DISCUSSIONThe PSO experiments for the portfolio optimization has been performed on threeunrestricted risky portfolio of 8 stocks,15 stocks and 49 stocks, and on three restrictedrisky portfolio of 8 stocks, 15 stocks and 49 stocks. Table 1 shows only the unrestrictedand restricted risky portfolio of 8 stocks together with each stocks daily ExpectedReturns (ER) and Standard Deviations (SD). All stocks are selected from the ShanghaiStock Exchange 50 Index (the SSE 50 Index). Individual stocks historical daily returnsare taken from 1st May 2009 to 3rd April 2009. Unrestricted portfolios do not haveconstraints on short selling. In other words, the proportion of an asset in the portfoliocould be negative or greater than 1.In order to evaluate the performance of PSO model, we compare PSO withanother heuristic Algorithm, Genetic Algorithm. In the experiments, PSO Solver hasbeen developed using Matlab as software development tool. Genetic Algorithm hasbeen developed using GeneHunter. Meanwhile, we also compare the result of themwith the result of the traditional method of VBA (Visual Basic Application).For the computation of the optimal risky portfolios, three approaches: PSOalgorithm, GA algorithm and VBA solver are implemented for the 8-stock, 15 stocks and49-stock portfolios. In the experiment of both PSO and GA, the size of the population is100 and the termination condition is 1000 iterations to find the optimal risky portfoliorespectively.The experiment shows that the SharpRatio value obtained by PSO is the best oneshown in Table 1. In the experiment, we find the SharpRatio value from GA is unstable.The efficient frontier obtained from PSO is absolutely the best. We also note that whenthe number of stocks is 8 and 15, the VBA solver could be better than GA. However, theGA algorithm is significantly better than VBA solver as the number of stocks increases.Table 1. 8 stocks unrestricted and restricted portfolios resultsPSOSolver:GASolver:VBASolver:
Risk Free
In conclusion, the performance of PSO algorithm is better than both GA and thetraditional VBA solver. From the experiments on unrestricted and restricted portfolios,the PSO solver clearly demonstrates the efficiency and effectiveness in solving highdimensional constrained optimization problems.CONCLUSIONS AND FUTURE WORKThe modified Knowledge Governance Framework has been developed and wepoints out that portfolio management could be taken as a tool for enterprise/organizationKnowledge Management. A fundamental principle of financial investments is- 186 -
e-Learning09diversification where investors diversify their investments into different types of assets.Portfolio diversification minimizes investors exposure to risks, and maximizes returnson portfolios. The paper focuses on solving the portfolio optimization problem in financeinvestment management. A metaheuristic Particle Swarm Optimization method hasbeen developed to optimize investment portfolios, which the objective functions andconstraints are based on both the Markowitz model and the Sharp Ratio model. In orderto make a valid comparison with other methods, different test problems were solved andthe results obtained when compared with the results of Genetic Algorithms (GA), VisualBasic for Applications (VBA) demonstrated the superiority of the PSO algorithm. Futureresearch may be conducted to further investigate the application of some derivedmodels or hybrid models of PSO to other investment strategy problems, for exampletracking the index and so on. Another further investigation may be put on methods forimproving the efficiency of the PSO solver for large portfolios in investmentmanagement.REFERENCES[1] Daniels, H. A. M. and Smits, M. T., Portfolio Optimization as a Tool forKnowledge Management, Operations Research Proceedings 2005, Springer BerlinHeidelgerg, 2006.[2] Davenport, T. H. and Prusak, L., Working knowledge: how organizationsmanage what they know, Harvard Business School Press, Bosten, USA, 2000.[3] Kennedy, J. and Eberhart R., Swarm intelligence, Morgan KaufmannPublishers, Inc. San Francisco, CA, 2001.[4] Maringer, D., Protfolio Mnagement with Heuristic Optimization, Springer, 2005[5] Markowitz, H., Portfolio Selection. The Journal of Finance, 7(1): p. 77-91, 1952.[6] Nahapiet, J. and Ghoshal, S., Social Capital, Intellectual Capital, and theorganizational advantage, Ac. of Man. Rev. (23) 2, pp. 242-266, 1998.[7] Sharpe, W. F., Mutual Fund Performance, Journal of Business 39 (S1), pp.119138, 1966[8] Smits, M. and De Moor, A., Measuring knowledge management effectivenessin communities of practice. In: proceedings of HICSS , (Ed: Sprague) IEE, Cal, 2004.[9] Wang, K., Hjelmervik, O. R. and Bremdal, B., Interduction to Knowledgemanagement - Principle and practice, Tapir Academic Press, Trondheim 2001.ABOUT THE AUTHORSDr. Yi Wang, Lecturer/Senior Lecturer, Nottingham Business School, Nottingham,NTU, UK, Phone: +44-1158488032 e-mail: email@example.com. Kesheng Wang, Professor, Department of Production and Quality Engineering,NTNU, Trondheim, Norway, Phone: +47-73597119, E-mail: firstname.lastname@example.com
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INTRODUCTIONNowadays when education from teacher-centered activity has become studentcentered activity the use of modern information and communication technologies inteaching and learning is a conventional and habitual practice. Not only teaching/learninghas changed, but also new trends have emerged into students testing, their knowledgeassessment and scoring. Introduction of such terms as computer-based assessment, eassessment, online assessment, and others may be considered as conclusive proof ofrecent trends. At the same time, with the dissemination of different forms of technologyenhanced learning, for instance, e-learning or m-learning, learning assessment hasbecome a constant concern [1]. Mainly two reasons are mentioned: lack of neededadaptivity and regularity of knowledge assessment. Even in traditional teaching andlearning, where adaptive and regular knowledge assessment may be carried out quitenaturally, usually only final tests and/or examinations are applied in practice due to thehigh workload of teachers. The same to the large extent is true also for computer-basedassessment.Traditionally it is based on tests where students receive a set of questions withalready pre-defined answers [2]. Questions mainly are multiple choice or multipleresponse questions, graphical hotspot questions, fill-in-blanks, text/numerical inputquestions and matching questions [3]. The well known and widely used e-learningsystems Blackboard (www.blackboard.com) and WebCT (www.webct.com) have built-inmechanisms to operate with tests. Along with indisputable advantages, such as greaterflexibility regarding place and time of assessment, providing assessment for largenumber of learners, instant feedback to learners [4], etc., these systems dont exploit allpossibilities that can be offered by computer-based testing. That is why computerassisted adaptive testing based on Item Response Theory appeared in [5]. In thisapproach students receive more difficult or easier test items, depending on theirprevious testing results, i.e. the sequence of test items depends on the answer given tothe previous test item. Thus, students knowledge levels are more accurately estimatedreducing negative psychological effects [5]. Besides, tests are shorter because feweritems are needed to obtain reliable results about students knowledge level.Despite of the range of abovementioned advantages even the usage of adaptivetesting doesnt support sufficiently wide and comprehensive knowledge assessment.Being explicit, tests dont allow to assess students knowledge structure, i.e. how he/sheunderstands relations between concepts or how new concepts are connected withpreviously mastered concepts [6, 7].The paper presents experience obtained from the use of the concept map basedadaptive knowledge assessment system (KAS) [6]. The KAS has been tested in severalengineering courses and learners feedback has been collected allowing continuouslyextending systems functionality. The remainder of the paper is organized as follows.The next section introduces basic conceptions of concept maps and the KAS. After thatit is described how the KAS has evolved during the years of its development. The key- 188 -
e-Learning09issues found from the feedback given by learners are highlighted which were used forexpansion of functionality of the KAS. Conclusions and future work are given in the lastsection.BASIC CONCEPTIONS OF CONCEPT MAPS AND THE DEVELOPEDKNOWLEDGE ASSESSMENT SYSTEMMost cognitive theories share the assumption that concept interrelatedness is anessential property of knowledge [8]. Cognitive theory underlying concept mapping grewout of Ausubels Assimilation Theory [9, 10] and Deeses associationism memory theory[11]. The former postulated a hierarchical memory structure, whereas the latterpostulated a network of concepts. A concept map (CM) a pedagogical tool developedby Novak [12, 13] is based on both abovementioned theories. According to Novak, aCM represents a part of an individuals cognitive structure, revealing his/her particularunderstanding of a specific knowledge area. Essentially CMs are a specific kind ofmental models that are used for representing and measuring of individuals knowledgelevel. Mathematically defined and visualized, a CM is undirected or directed graphconsisting of a finite, non-empty set of nodes which represent the concepts of aknowledge domain, and a finite, non-empty set of arcs (undirected or directed) whichrepresent the relationships between pairs of concepts. Arcs may have the same ordifferent weights, i.e., from the teachers (experts) point of view some relationships maybe more important than others [14]. A CM can be defined also as an attributed graph(attributes can be words or linking phrases (propositions) used to specify the kind ofrelationship between concepts [15]). A proposition is a semantic unit of CM, i.e., aconcept-relationship-concept triple which is a meaningful statement about some objector event in a problem domain [16]. An example of CM constructed for the subjectSystems Theory Methods is given in Figure 1.Planning
part of
Objectivepart of
Control and/ormanagement
useSystem thinking
part ofis adivide from
is aSystem conceptsis a
Relationshipis a
is a
System science
is based on
Resourcespart of
A whole
is aOutput
is aSet
is aProcess
is ais a
Inputis a
an important relationship
Serial inputFeedbackinput
e-Learning09teacher created and learners completed CMs, as well as learners final scores arestored (Figure 2).The developed system supports the following scenario. A teacher divides a studycourse into N stages and defines all concepts and relationships between them. Thesystem supports teachers actions for drawing CMs for each stage on the workingsurface. During knowledge assessment or self-assessment learners get a task (a CM)that corresponds to the current stage of learning process. After finishing the completionof CM, a learner confirms his/her solution and the system compares CMs of the learnerand the teacher on the basis of patterns of learners solutions [18]. The final score andthe learners CM are stored into the database, and a learner receives feedback aboutcorrectness of his/her solution.DatabaseAdministratorsmodule
e-Learning09The students pointed out that textual format of feedback isnt informative enough, andsuggested to elaborate the feedback in order to identify mistakes in a graphical form.They also suggested using drag-and-drop technique.Based on students evaluation several new solutions were implemented in thesecond version of KAS. First, more informative feedback for teachers and learners wasimplemented. For teachers the system collects statistical data about non-existing linksthat learners often define, about correct links that learners define rarely, and aboutincorrect weights of particular links. The feedback for learners was more instructive andwas given in a graphical form. A learner receives his/her completed CM with labelsrepresenting received points for each link.Second, two approaches for changing the degree of task difficulty were developed,namely, inserting additional concepts into a CM by the system or offering different typesof tasks. In the first approach only fill-in-the-map tasks are used, and a learner can askto reduce the degree of task difficulty. The agent-expert uses the developed algorithm,identifies degrees (the number of incoming and outgoing arcs) of free nodes (nodeswhere concepts are not placed yet), and sorts them in descending order. The node withan average index is chosen and the KAS inserts the corresponding concept into alearners CM. Nodes with an average degree are inserted first (the decision is based onthe assumption that concepts with smallest degree give too little help, but concepts withhighest degree are keywords that learners must know). This approach was evaluated in4 study courses (3 engineering and 1 pedagogical) both in Riga Technical Universityand Vidzeme University College. Forty four students took part, and 35 questionnaireswere received after knowledge assessment. The questionnaire included 17 questions.Questions about new possibility offered by the system to reduce the degree of taskdifficulty were added. Students were asked did they use it, and if not, why. In thisexperiment 26 (74%) students answered that CMs help them to understand learningmaterial better, but for 21 (60%) student CM tasks were difficult, and for 4 (11%) verydifficult. At the same time only 10 (29%) students used the systems offered possibilityto reduce the degree of task difficulty, while others answered that they didnt want toreduce their total score. Practically all students found the new graphical interfaceconvenient, perceivable, and demonstrative. Twenty seven (77%) students indicatedthat systems provided feedback was useful showing what kind of knowledge is missing.So, evaluation of feedback was significantly higher in comparison with the first versionof the KAS.In the second approach 3 fill-in-the-map and 2 construct-the-map tasks are usedstarting with high-directed one where a structure of a CM, which is visualized as anattributed graph, is given, and ending with low-directed task where a CM must be drawnin case if only a list of concepts is given [20]. A learner receives a task that has ateachers defined degree of difficulty. During the task performance a learner can ask toreduce the degree of task difficulty or depending on learners results the system can doit. If a learner has reached a teachers predefined minimal score without reduction of thetask difficulty, the system delivers more difficult task at the next stage. This is the KASsadaptive mechanism. The second approach was evaluated using the KAS for oneengineering course Fundamentals of Artificial Intelligence. Totally 30 studentsparticipated and 28 questionnaires were received. Again approximately the samenumber of students (19 or 68%) answered that CM tasks helped them to understandbetter logical organization and interconnectedness of mastered concepts. Sixteen (57%)found CM based tasks difficult, and 12 of them used possibility to reduce the degree oftask difficulty. The system increased the task difficulty for 9 students. Questionnairesreflected suggestions of students among which the most significant were option todefine synonyms of concepts and linking phrases and possibility to used directedgraphs which may make easier the task of definition of linking phrases.- 191 -
e-Learning09These suggestions which were implemented in the third version of the systempromote further expansion of KAS functionality. Besides, a set of standard linkingphrases was defined, such as is a, is instance of, has attribute, has value, andpart of. Performance of the third version was evaluated in the study course SystemsTheory Methods by 40 students. In 37 submitted questionnaires with 22 questions (3questions were added asking about suggestions how to improve the feedback) therewere 57% answers that for 21 student it was difficult to solve given CM based tasks,while 16 (43%) of them found it easy. As the main reason of difficulties in 10questionnaires was mentioned insufficient learners support from the systems side.Students wanted to get some learning material from the KAS if they have difficulties tosolve the task.Thus explanation of the concept is added in the fourth version of the KAS.Students may receive explanations in three forms a definition, a short description oran example of concept. The initial form of explanation is chosen by a learner who canchange it during solution of CM task. Moreover, the system keeps track of learnersactions and determines which form of explanation has the greatest contribution tocreation of correct CM. This, in turn, enables modification of students model. Thisversion was evaluated using more informative questionnaire that contains 33 questions.Students were asked for their opinion, for example, why they found working with CMsdifficult or easy, did they find that after the reduction of task difficulty they really receivedeasier task, did provided explanations helped to solve the task, etc. The version wastested by 36 students who learned Systems Theory Methods. It is interesting to pointthat rather frequently students found that for them construct-the-map task with given listof concepts seemed more easy than fill-in-the-map task in which linking phrases mustbe defined. Testing of the fourth version of KAS shows that approximately the samenumber of students used each of the proposed explanation forms. At the same timethey found that definitions are the most useful for better understanding but exampleswere not helpful enough.At the moment the fourth version of KAS is transformed into new three-tierarchitecture for security reasons [18]. This new version is implemented using thefollowing technologies: Eclipse 3.2, Apache Tomcat 6.0, Posture SQL DBMS 8.1.3,JDBC drivers, Hibernate, VLDocking, JGoodies and JGraph.CONCLUSIONS AND FUTURE WORKThe paper reflects the experience get from testing four versions of adaptive KAS indifferent study courses. Testing results allowed step-by-step to expand functionality ofthe developed system. Obtained experience manifests that the KAS based on CMshelps students better to understand learning contents. In all testing experiments inaverage around 75% of students liked the usage of CMs for knowledge assessment andthey wanted to use this approach in other courses, too. At the same time it turns out thatconsiderable number of students (more than 55%) found that CM based tasks aredifficult for them. As main reasons of difficulties insufficient learners support from thesystems side, lack of work experience with CMs and insufficient knowledge of learningmaterial are mentioned more frequently. Contrary, those students who didnt haveproblems stressed that they have good understanding of nature of CMs, haveexperience of drawing different diagrams and using many software products, as well ashave good knowledge.It is interesting to stress that engineering programme students who studycomputer science achieved considerably higher scores in comparison with students ofpedagogical programme. The working hypothesis is that computer science students arefamiliar with various diagrams used in software engineering therefore a CM is not a newtechnique for them, but more experiments are needed for acceptance of it.- 192 -
e-Learning09These testing results have inspired the developers of KAS to continue itsimprovements. Future work is directed towards extension of the developed KAS. Thealgorithm for transformation of study course ontology into CMs should be integrated intothe system. New algorithms for more efficient CMs comparison should be developedand implemented, too. Moreover, more rich and complete student model should bedeveloped and used. And last but not least, a scoring system by which students CMscan be evaluated accurately and consistently, taking into account various cases ofreduction of the degree of task difficulty should be worked out.REFERENCES[1] F. E. L. da Rocha, J. V. da Costa, Jr., E. L. Favero, An approach to computeraided learning assessment, in Proceedings of the Third International Conference onConcept Mapping, 2008, pp. 170-177.[2] Computer-assisted Assessment (CAA) Centre. [Website]. Available at:http://www.caacentre.ac.uk/[3] Designing effective objective test questions: an introductory workshop, CAACentre, Loughborough University, 1999. [Online document]. Available at:http://www.caacentre.ac.uk/dldocs/otghdout.pdf[4] G. Lambert, What is computer aided assessment and how can I use it in myteaching? Canterbury Christ Church University College, 2004. [Briefing paper].[5] E. C. Papanastasiou, Computer-adaptive testing in science education, inProceedings of the 6th International Conference on Computer Based Learning inScience, 2003, pp. 965-971.[6] J. Grundspenkis, Development of concept map based adaptive knowledgeassessment system, in Proceedings of IADIS International Conference on e-Learning,2008, pp. 295-402.[7] J. Grundspenkis, Knowledge creation supported by intelligent knowledgeassessment system, in Proceedings of the 12th World Multi-Conference on Systemics,Cybernetics and Informatics, June 29-July 2, Orlando, Florida, USA, 2008, pp. 135-140.[8] M. A. Ruiz-Primo and R. J. Shavelson, Problems and issues in the use ofconcept maps in science assessment, Journal of Research in Science Teaching, vol.33, no. 6, 1996, pp.569-600.[9] D. P. Ausubel, Educational Psychology: A Cognitive View. New York: Holt,Rinehart and Winston, 1968, p. 685.[10] D. P. Ausubel, The Acquisition and Retention of Knowledge. New York:Kluwer Academic Publishers, 2000, p. 232.[11] J. Deese, The Structure of Associations in Language and Thought. Baltimore:Johns Hopkins Press, 1965.[12] J. D. Novak and D. B. Gowin, Learning How to Learn. New York: CornellUniversity Press, 1984, p. 150.[13] J. D. Novak, Learning, Creating, and Using Knowledge: Concept Maps asFacilitative Tools in Schools and Corporations. Mahwah, NJ: Lawrence ErlbaumAssociates, 1998, p. 272.[14] M. Ahlberg, Varieties of concept mapping, in Proceedings of the FirstInternational Conference on Concept Mapping, Pamplona, Spain, 2004. Available at:http://cmc.ihmc.us/papers/cmc2004-206.pdf[15] F. S. L. De Souza, M. C. S. Boeres, D. Cury, C. S. De Menezes, and G.Carlesso, An approach to comparison of concept maps represented by graphs, inProceedings of the Third Conference on Concept Mapping, 22-25 September in Tallinnand Helsinki, 2008, pp. 205-212.[16] A. J. Caas, A summary of literature pertaining to the use of conceptmapping techniques and technologies for education and performance support,- 193 -
e-Learning09[Technical Report Submitted to the Chief of Naval Education and Training, Pensacola,FL], 2003, p. 108.[17] A. Anohina and J. Grundspenkis, Prototype of multiagent knowledgeassessment system for support of process oriented learning, in Proceedings of the 7thInternational Baltic Conference on Databases and Information Systems, 2006, pp. 211219.[18] J. Grundspenkis and A. Anohina, Evolution of the concept map basedadaptive knowledge assessment system: implementation and evaluation results,Scientific Proceedings of Riga Technical University, 5th series, Computer Science,Applied Computer Systems, Vol. 38, 2009, pp. 13-24.[19] A. Anohina, E. Lavendelis, and J. Grundspenkis, Concept map basedassessment system with reduction of task difficulty, in Proceedings of the 16thInternational Conference on Information Systems Development, 2007, pp. 853-866.[20] A. Anohina, D. Pozdnakovs and J. Grundspenkis, Changing the degree oftask difficulty in concept map based assessment system, in Proceedings of the IADISInternational Conference e-Learning 2007, 2007, pp. 443-450.ABOUT THE AUTHORProf. Janis Grundspenkis, Dr.habil.sc.ing., Department of Systems Theory andDesign,RigaTechnicalUniversity,Phone:+37167089581,E-mail:firstname.lastname@example.com
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INTRODUCTIONNowadays the measurement problems are solved using visual engineeringenvironments. They allow the creation of complete software solutions by linking visualgraphical objects (icons) instead of the traditional use of specialized programminglanguages. They simplify the decision of problems of the type of consecutive tests,management of equipment and development of user interface. These environmentscombine a set of objects that help the recording, analysis and presentation of data.Table 1 shows some of the widely used programming environments for development ofmeasurement systems. In short are presented their versions, prices, licenses, theoperating environment in which they work and the company that developed each ofthem.Table 1Software
Last version
9.0 /2008
10.0 / 200711.0 / 2008
8.6 / 2008
9.0 / 2008
R2008a /2008
Price$1800(Commercial) /$150 (Student)$2499(Commercial) 2299(Commercial)$1199(Commercial) /$99.95(Student) 2499(Commercial)$1900(Commercial) /$99 (Student)
Windows,Mac OS X,Linux
Windows,Linux, SunWindows,Mac OS X,Linux
7.0 / 2006
6.0 / 2008
e-Learning09LAYOUTThe slogan of the National Instruments corporation, one of the leading companiesin manufacturing apparatus and software for experimental research and measurements,is "The Software is the Instrument", i.e. "The software is a base of the measurementinstruments". This slogan shows the importance of the software as a basis and a tool fordesign, completion and operation of any modern intelligent system for collecting dataand conducting measurements.Due to the particularities of the scientific research most widely used are the socalled virtual measurement environments (VME) [12].A key element of VMEs, without which they generally can not exist, is the softwarefor collecting and processing of measured data. VME differs in its features, problemorientation and level of the software use. Quality and flexibility of the software used forthe development of systems for experimental research ultimately determine the qualityand usefulness of the systems as a whole.These environments are designed to control various measuring devices andembedded circuits for input-output of analog and digital signals, and allow collection,analysis and visual presentation of measurement information. The standard set offunctions realised using these environments includes:
e-Learning09The class of virtual measurement environments includes a wide range of products:from the powerful software systems for data collection (practically comparable with thenetwork super environments) to simple tools which implement a minimum set offunctions necessary to collect data from embedded circuits and devices. Depending onthe completeness of these features the software can be divided in the following groups:environments with large, medium or small capacity (Fig.1).
X86 - MS-DOS, Windows 9x, OS/2, Windows NT, Windows XP, Windows Vista,Linux; SunSPARKstation - Sun OS, Sun Solaris; Hewlett Packard workstations HP-9000 Series 700 - HP-UX; Apple computers - Mac OS;- 197 -
UNIX - X-Windows; DEC Alpha - Open VMS and OSF/l.The specialised environments (Fig.1) work on certain configurations of equipmentand apparatus and actually are software for a specific measurement system. Anexample for such environment is DADiSP for autonomous measurement system ACQwhich is produced by Finn Metric [8] and Type 3550 Multi Channel Analysis SystemSoftware for multi-type measurement system type 3550 produced by Bruel & Kjaer [13].The functionality of the virtual measurement environments may be significantlyextended by including of problem-oriented packages (Fig.1). These packages containsubprograms which realise a set of functions and usually are implemented as dynamiclink libraries (DLL).The problem orientation of the packages covers many different areas. They solvesuch problems as experimental research control, testing, database control, statisticalanalysis of random processes, mathematical processing and approximation ofdependences, spectral analysis, signal generation, image processing and others.Many problem-oriented packages contain build-in functions and tools for creationof new objects and user subprograms.Instrument packages (Fig.1) are used for the development of virtual measurementenvironments. These packages can be separated into packages, which extended:
e-Learning09CONCLUSIONS AND FUTURE WORKThe choice of particular software type for development of experimental researchsystem is determined by the following basic criteria:
e-Learning09[14] The intelligent solution for data acquisition // Microstar laboratories, 2004.[15] Total solution for PC-based industrial automation // Solution guide. 2005, Vol.51. /Advantech.ABOUT THE AUTHORSAssoc. Prof. Tsvetozar Georgiev, PhD, Department of Computing, University ofRuse, Phone: +359 82 888 827, -mail: firstname.lastname@example.com. Prof. Georgi Krastev, PhD, Department of Computing, University of Ruse,Phone: +359 82 888 672, -mail: firstname.lastname@example.com
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INTRODUCTIONOver the last decade, the importance of information quality (IQ) is ever morerecognized among practitioners and academics. It has developed beyond the traditionalview of IQ as a synonym for data accuracy. Wang and Strong (1996) note that in orderto improve data quality in an organisation, a multi-dimensional view of the concept mustbe taken.Many researchers have examined IQ. The result of the plethora of publications isa multiplicity of descriptions, definitions, criteria lists, case studies and frameworks forvarious areas of application (e.g. Wang, Storey & Firth 1995). In addition to theseframeworks, literature on IQ indicates a number of technical, managerial, andorganisation factors that are believed to improve IQ. However, at present there are fewtools used for IQ training and education. Furthermore, there is no practical tool focusingon teaching IQ skills or competences. In order to provide a tool aiming to increase theawareness of IQ, we propose a teaching tool to show practical application of IQ.This paper is organized as follows. Section 2 provides a review of IQ research tohighlight the development of IQ. Followed by the review, section 3 proposes a teachingtool that can be used to show the effects of IQ in organisations. In order to evaluate thetool, section 4 presents some comments received from users using our tool. Finally,section 5 concludes this paper by summarizing our experiences and outlines the furtherdevelopments.LITERATURE OVERVIEWOver the last decade, the importance of information quality (IQ) is ever morerecognized among practitioners and academics. It has developed beyond the traditionalview of IQ as a synonym for data accuracy. Wang and Strong (1996) note that in orderto improve data quality in an organisation, a multi-dimensional view of the concept mustbe taken.Over the last decades IQ became an important area both in research and practice.As recent job openings indicate, many organisations require professionals withexpertise as IQ Manager, IQ Analyst, IQ Consultant, and IQ System Developer.Addressing the growing demands for qualified IQ professionals, taught courses andresearch programmes were initiated at some Universities. One example for suchprogrammes is the IQ Program at the University of Arkansas at Little Rock (USA).Professional training courses are offered from various organisations, such as the IQProgramme at the Massachusetts Institute of Technology, Training programmes fromthe International Association for Information and Data Quality or Information ImpactInternational, Inc.Content taught within these programmes can build on an extensive foundation ofIQ research. From the middle 1990s to present, IQ research becomes intensive,systematic and empirical. Therefore, the amount of IQ papers significantly increases in- 201 -
e-Learning09a wide range of journals and conferences. From 1995 to 2008, more than 15 IQ booksare published. These books have addressed different aspects of IQ research. Three IQjournals have been launched so far: Data Quality Journal in 1995, International Journalof Information Quality in 2007 and ACM Journal of Data and Information Quality in2008. Also, many leading database and information system conferences such asSIGMOD, VLDB and CAiSE have included IQ as one of the conference themes.Furthermore, since 1996, International Conference on Information Quality (ICIQ) isannually held to provide a forum for researchers and practitioners to present researchfindings and exchange IQ knowledge.From our review, we identified following main concept and key points that currentIQ training programmes and courses contain or aim to achieve:
e-Learning09We extended the traditional Beer Game and included IQ aspects in form of variousmarketing and sales information to the students. The system is designed, to informationof different quality levels. Using the given information, students are asked to makeinventory control decisions.This supply chain game involves manufacturer, distributor and customer. Theparticipants are asked to play the distributor and the other two roles are taken over bythe computer. According to the inventory information and customer ordering history,participants will order products from the manufacturer and supply products to thecustomer. There are two sources of cost associated with the game: (1) If the distributorcannot fill customers order, a cost will be occurred (1 euro per unfilled item). (2)Whenthe items are stored in the inventory, a cost will be occurred (0.5 euro per stored itemper week). The goal of the game is to minimize the cost in the distributor's inventorymanagement.The interface of our teaching tool is shown in Figure 1. In order to start the game,participants place an order in the input box and click the OK button. The order is sentto the manufacturer. The procurement delay from manufacturer takes 3 weeks. Thatmeans it takes 3 weeks from making the order and receiving the ordered items. Notethat the manufacturer may not be able to fill the order if this order exceeds the inventoryof the manufacturer. For example, in week 10, participants place an order to themanufacturer. This order will arrive to the manufacturer in the beginning of week 11.The manufacturer needs 1 week to process the order. In the beginning of week 12, themanufacturer ships the ordered items to the distributor and the shipment takes 1 week.Therefore if participants make an order in week 10, the order will arrive in week 13.
e-Learning09the information completeness is 80%, the participant made 10 ordering decisions andthe total cost is 356 euro.The measurements of completeness and accuracy are organised as follows. Thelevels of completeness are expressed by percentages. The percentages can be determined by the equation 4-5. In the experiment, 5 completeness levels are used: 20%,40%, 60%, 80% and 100%. For example, 60% completeness means only 60% of theinformation is provided.
e-Learning09Nanyang Technological University, University of Dundee, Microsoft Research Asian,Hibernia Atlantic Ltd., Avaya Ireland, and J.P. Morgan UK. 65% of subjects were maleand the other 35% are female. The average age of subjects is 32.All the participants works or studies are related to information processing. Thisinformation can be presented by text, audio, video, conversation etc. The most commoninformation processing concerned checking emails and collecting business information.Some participants work concluded with a report. This report can be considered as aresult of gathering and processing different information. Therefore IQ is directly relatedto the quality of their work. Some participants work is centred on data analysis andinformation management. IQ is vital since poor quality information may generateerroneous analysis results, which could incur a variety of business losses.After the tool evaluation, we carried out a semi-structured interview to theparticipants. This interview consists of two major themes: experience of using the tooland opinions towards the IQ problems. After collating the answers, we organise andinterpret the feedbacks as follows.Through using this tool, the interviewees stated that this tool is a practicaldemonstration to show the effects of IQ. Compared to theoretical training, the toolincreases the interest and understanding to IQ concepts. It also helps the participants toraise the awareness of IQ management. Many interviewees emphasized that IQ isobserved as a crucial factor to the success of information systems. Organisations needto cultivate IQ concepts into organisational cultures.Additionally, the interviewees found the tool is easy to use and can help non-IQprofessionals to quickly understand IQ concepts. For example, they realize that IQ canbe measured by different dimensions such as accuracy and completeness. Importantly,after using the tool, the participants understood how information accuracy andcompleteness perform in a practical application. Therefore the interviewees concludedthat improving IQ could enhance their work performance. Organizations need toincrease the awareness of IQ management.When concerned with IQ problems, Most of the interviewees experienced IQproblems in their work or daily life. Interviewees provided us with various examples ofsuch IQ problems. Compiling their feedbacks, we found that IQ problem is highlypervasive in work and everyday life. Hence it is valuable to improve IQ in peoples workand daily life. However, although all the interviewees have met different kinds of IQproblems, only a few people had considered IQ improvement. Some interviewees havegrown accustomed to IQ problems and were not aware of the possibility of improvingIQ. Some student interviewees complained that IQ problems always happened withintheir life and study, especially with regards to information from the internet. Someindustrial interviewees had considered improving IQ in their company. However due tounknown budgets and the inexistence of mature IQ management models, highmanagement always denied proposals for systematic IQ improvement. The collectedfeedbacks showed that although many IQ problems exist, the operation of IQimprovement procedures is very rare.In order to find the root causes of the IQ problems, we collect the possible reasonsof triggering an IQ problem. Except some typical causes such as typing errors ordelayed input, interviewees are mainly concerned with two causes: system design andinformation processing. Some interviewees emphasized that it is important to prevent IQproblems when designing the system. For example, the lack of constraint checking inthe system can result in poor-quality data in the database. Some intervieweesconsidered that IQ problems can be generated in the information processing ortransferring procedure. Therefore it is critical to increase IQ awareness when weprocess or transfer the information.Besides the feedbacks above, we also collected the suggestions for theimprovement of this tool. The further improvements for the tool can focus on the group- 205 -
e-Learning09competition. That means each component of the supply chain can be played by oneparticipant. The participants in the same supply chain are composed as a group. Thusdifferent groups are able to compete for their performance by comparing the benefit in adetermined period. In addition, we can extend the tool by including more IQ dimensions.It would enable students to observe how different IQ dimensions perform in a practicalapplication.CONCLUSIONIn this paper, we describe a teaching tool to facilitate IQ education. The tool isbased on the traditional Beer Game, originally developed at the Massachusetts Instituteof Technology. We adapted this game and introduced IQ elements to the game. Thetool was developed in the context of an undergraduate course in Information SystemsStrategy. The course is designed as a problem-oriented course. In order to receivefeedback from professionals and academics, we invited 30 subjects to use the tool andprovide feedback. By using our tool the participants showed an increased awareness ofIQ and its importance. In addition, they have understood that IQ is multi-dimensionalconcept. Although the initial feedback shows the benefit of using our tool, it is anongoing project. We aim to improve and extend its functionality and formally incorporatethe tool in our undergraduate course.REFERENCES[1] Batini, C. and Scannapieco, M., Data Quality, Concepts, Methodologies andTechniques, Publisher: Springer, 2006.[2] Brodie, M. L. , Data quality in information systems. Information andManagement, 3(6), 1980, pp. 245-258.[3] Chaffey, D., Wood, D. Business Information Management: ImprovingPerformance using Information Systems, Prentice Hall, 2005.[4] DeLone, W.H. and McLean, E.R. , Information system success: the quest fordependent variables, Information System Research, 3(1), 1992, pp. 60-96.[5] Kaminsky, P. and Simchi-Levi, D., A new computerized beer game: a tool forteaching the value of integrated supply chain management. In Supply Chain andTechnology Management, Hau Lee and Shu Ming Ng, eds., The Production and Operations Management Society: Miami, Florida, 1998.[6] Khalil O.E.M., Strong D.M., Kahn B.K., Pipino L.L., Teaching information qualityin information systems undergraduate education, Informing Science, 2(3), 1999, pp 5359.[7] Paradice, D.B. and Fuerst, W.L., An MIS data quality methodology based onoptimal error detection, Journal of Information Systems, 5(1), 1991, pp. 48-66.[8] Wang, R.Y. and Strong, D. M. , Beyond accuracy: what data quality means todata consumers. Journal of Management Information Systems, 12(4), 1996, pp. 5-34..[9] Wang, R.Y., Storey V.C. and Firth, C. P., A framework for analysis of dataquality research, IEEE Transactions on Knowledge and Data Engineering, 7(4), 1995,pp.623-640.[10] Yu, S., and Neter, J., A stochastic model of the internal control system,Journal of Accounting Research, 1(3), 1973, pp. 273-295ABOUT THE AUTHORSMarkus Helfert, PhD, School of Computing, Dublin City University, Phone: +353 1700 - 8727, -mail: email@example.com Ge, School of Computing, Dublin City University, Phone: +353 -1700 8980, -mail: email@example.com- 206 -
INTRODUCTIONIt is well known that training simulations are one of the most appropriate ways toincrease learner's engagement. A survey, described in [1] shows that using aninteractive simulation exercise results in increased student engagement levels. Iverson[2] also suggests that building of learner engagement requires incorporation ofsimulations into training process. This largely applies to the Millennial students thatdistinctly show preferences to first-person learning (by doing, exploring, and discovery)[3].Having incorporated simulations in the training process, we have to go further andconsider how to increase the students' engagement to the simulations themselves.What is the motivation for this?The Virtual Laboratory (VLab) on Computer Organization was implemented in2002 and has been intensively used within practical workshops on the correspondingcourse. The simulators, included in the VLab were designed for highly motivatedstudents having preliminary knowledge on the subject matter. During the first years ofadoption the simulators used to achieve a good deal of user engagement. The studentswere enthusiastic to control the simulations and when completed an operation theywere curious to play the simulation again and to accomplish the task with betterperformance with less or no mistakes, and for shorter time.Recently we have remarked that the students' engagement and motivationdecreases year after year. More and more students find the simulators difficult and donot feel like trying again to perform better.Out of doubt, our students have changed and we have to revise and reorganizeour training tools in order to keep them engaged. The Millennial students wouldappreciate game alike training tools not in terms of GUI, but in terms of userinteractions.It is obvious we could not teach foundations of Computer Arithmetic using 3Dvirtual worlds, but we could adopt an interaction manner, inherent for a computer game(CG). Gaming elements provide motivation, structure, and a goal, and also create acompetitive environment for learning. Including gaming elements, like score-keeping,competition, and surprise variables can increase the entertainment value and fun of asimulation [4]. Most of CG offer to the players to select a level of difficulty and after asuccessful action, the player gets a kind of a bonus. He (she) is also provided withinstructions and directions on each step of the game.Each simulation must have a way to judge and direct learners. In technologybased simulations, the design and sophistication of decision engine is the key to thesimulations success [4]. In this paper we describe our approach to improve the existingtraining simulators on Computer Organization in the context of presented abovediscussions, and to make them adaptable to students abilities. This will be achieved- 207 -
e-Learning09through designing a new decision engine for the simulators and upgrading their userinterface (UI).RESEARCHWhy is necessary to seek after adaptivity? That is because the Millennial studentsare addicted to environments adapting to their interests, needs and abilities, so theynaturally expect the same from their training tools. Before designing the set of trainingscenarios, we have to consider the level of difficulty for each one. Niehaus and Riedl [5]present the concept that Zone of Proximal Development (ZPD) (Fig.1) being adevelopmental theory, could be applied to training scenarios as well. It provides arepresentation of the balance between learner ability and scenario difficulty. The idealsituation is when the learner does not leave out the ZPD into the zone of confusion orzone of boredom. A training scenario is considered effective if falls within the ZPD (thepatterned zone in Fig.1). A similar idea shares Sierra [6] providing a visualrepresentation of challenge/ability balance (Fig.2). According to her, the best userengagement is achieved when the challenge, provided by an application is adequate tousers ability.
The next consideration is about the way of selecting the most appropriate tostudents ability scenario. There are two approaches possible. The first one gives theresponsibility of level selection to the system. In this case the student completes a test,and on the basis of the test results, the system defines the appropriate level of difficulty.Giving account to the short attention spans and expectation of instant gratification thatare inherent for Millennial students as well as their trend to overestimate their efforts,this approach is not likely to work at 100%, because the students may refuse tocomplete the test. The Millennial students prefer to have at disposal an array of choices,so the idea to put the level selection to their responsibility, seems to work better.The last consideration is focused to the user interface. It should be reorganized tousers with visual-kinesthetic learning style (that is actually the Millennials one). In thisconnection we have to seek after: graphical representation of concepts, visual rathertextual hints and help, enhanced animation. Since the most of our future student aregame-addicted and take the learning as a fun, they would appreciate getting rewardsand interaction style that is not so academically rigorous.
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e-Learning09DESIGN AND IMPLEMENTATIONThe existing simulators, included in VLab are implemented as separateapplications of two types those that simulate how the arithmetic operations areexecuted in ALU, and simulators of the main CPU blocks and of various types of CPU.In this paper we discuss the improvement of the first type simulators. The arithmeticsimulators will be integrated into a single application. The decision engine will operate intwo layers external, that controls applications behaviour, and internal that drives theselected simulation itself. Therefore, the design is organized in two flows. The externallayer of decision engine (Fig.3) deals with selection of operation that is to be simulated,selection of micro-algorithm for operation execution, selection of difficulty level and atthe end of the session saving the students performance (student ID, date, time,scores achieved).
See Figure 4
e-Learning09As could be seen on Figure 3, when the application is launched a start screenappears and the student is able to select one of the basic arithmetic operations,executed by ALU (addition, multiplication, division, etc.) that is of interest to him (her).After selection of operation, the student is offered to select a simulator that reproducesone of the known micro-algorithms for executing the selected operation. After thesimulator has been selected, it is necessary to be defined the simulators level ofdifficulty. There are 5 levels provided (Fig.4) and they are actually tied together with theself-estimated level of knowledge of the student. Level 1 is relevant to Lack ofknowledge option, as level 5 fits to Good knowledge option. To each level is assigneda scenario, describing usersimulation interaction. When the difficulty level is defined,the simulator is started in the appropriate mode. To avoid students self-overestimation,when a level is selected, and the previous one is not already passed by the student, he(she) will be asked to play it once to show ability to manage with the desired level.
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e-Learning09THEN SET_PARAMETER (Ld_Rx : Disabled ; Ld_Ry : Enabled)Rule 2: IF ((Ld_Rx : Enabled)AND (Control ID <> Ld_Rx))THEN SET_HINT (Hint : The correct MO is: Load Rx.)
Rule 5: IF (Xn = 1)THEN SET_PARAMETER (Out_Rz : Enabled)Rule 6: IF (Xn = 0)THEN SET_PARAMETER (Out_Rz : Disabled ; Out_Ry : Disabled;SR_Rz : Enabled)Rule 7: IF ((Out_Rz : Enabled)AND (Control ID = Out_Rz))THEN SET_PARAMETER (Out_Rz : Disabled ; Out_Ry : Enabled)Rule 8: IF ((Out_Rz : Enabled)AND (Control ID <> Out_Rz))THEN SET_HINT (Hint : The correct MO is: Output code Rz.)
Algorithmic parametersLd_Rx : DisabledLd_Ry : DisabledOut_Rz : EnabledOut_Ry : DisabledSt_Rz : DisabledSR_Rz : DisabledSR_Rx : Disabled
The Inference engine (rule interpreter) applies rules to the available information inthe workspace. The rule interpreter uses forward chaining inference method startsfrom the initial content of the workspace and works forward to the conclusions. Itsearches the rule base until it finds a rule where the IF clause is known to be true.When found it can conclude, or infer, the THEN clause, resulting in the addition of newinformation to the workspace.
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e-Learning09CONCLUSIONS AND FUTURE WORKIn this development we have chosen to put the responsibility of difficulty levelselection to the students, basing on the assumption that this solution is more relevant toMillennials learning style. As future development we are planning to design andimplement a decision engine that determines what level of difficulty is most appropriateto students ability after analyzing the results, achieved by the student on completion ofpreliminary test. Such a test should be completed on each level change as the decisionengine will select random test questions from a database. The final idea is acomparison of these two approaches and pointing out the better and more effective one.The conclusion will be drawn on the base of an inquiry among the students, as well asthe stored students performance.REFERENCES[1] Bulger, M., R. Mayer , K. Almeroth, S. Blau. Measuring Learner Engagement inComputer-Equipped College Classrooms. Journal of Educational Multimedia andHypermedia. 17 (2), 2009, pp. 129-143. Chesapeake, VA: AACE.Retrieved from http://www.editlib.org/p/23524.[2] Iverson, K. Engaging the E-Learner: Interaction is Not Education. LearningSolutions e-Magazine, February 25, 2008.[3] Oblinger,D.G., J.L.Oblinger. Educating the Net Generation, EDUCAUSE, 2005.Available online at: www.educause.edu/educatingthenetgen/.[4] Vaughan, M., K. Himmel. FORCES SHAPING OUR FUTURE - ELEMENTSOF SIMULATION , White Paper, Regis Learning Solutions, 2006.[5] Niehaus, J., M. O. Riedl. Scenario Adaptation: An Approach to CustomizingComputer-Based Training Games and Simulations. Proceedings of the AIED 2009Workshop on Intelligent Educational Games, Brighton, UK, 2009.[6] Sierra, K. Can you have too much ease-of-use? Creating passionate users,Available online at:http://headrush.typepad.com/creating_passionate_users/2005/03/can_you_have_to_1.htmlABOUT THE AUTHORSAneliya Ivanova, Principal Lecturer, PhD, Department of Computing, University ofRousse, Phone: +359 82 888 827, -mail: firstname.lastname@example.com Yakimova, Principal Lecturer, PhD Student, Department of Computing,University of Rousse, Phone: +359 82 888 276, -mail: firstname.lastname@example.com work was partially supported by the European Social Fund and the BulgarianMinistry of Education and Science under the Operation programme Human ResourcesDevelopment, Grant # BG051PO001/07/3.3-02/7.
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INTRODUCTIONData modelling is the most difficult and essential process that represents visuallydata organization in Database Management System (DBMS). A data model that iswrongfully formulated could make the implemented application ineffective and useless.That is why every database course begins with an introduction of data design conceptsand gives a theoretical foundation of Entity-Relationship (ER) and relational models.However, some students, especially those involved in distance learning courses, findtheoretical notions and rules difficult to understand. A better way for students tocomprehend the theory is to put it in practice. Designing small learning units that containa short theoretical basis, examples, and tasks to be performed appear to be mostsuitable in distance education [2] as well as in e-Learning education. Hence, the need ofinteractive data modelling tool for learning arises. Existing data modelling tools allowdatabase designers to create, visualize, and save complex data structures, but they arenot suitable for visual explanation of the design modelling principles and stages.The idea is to design and implement an educational computer-tool that willillustrate the transformation of an Entity-Relationship diagram (ERD) into a relationalschema. The tool is developed for educational purposes mainly and not as a powerfulmodelling tool for enterprise projects.The commercial products [6,7,8,9] offer to users a wide range of functionalities thatcould be applied for a professional usage, rather than gaining fundamental knowledge.These products are very expensive for educational purposes.On the other hand, researches in the field of designing data modelling tools forlearning are conducted in some universities. A web-based tool is proposed in [5] whichis developed to correct conceptual database schema automatically. A student has todesign an ER schema and enter it into the system through a user friendly interfacespecifically designed for it. The tool corrects the design and shows detected errorsgiving advice on how to solve them. Another approach is an e-learning tool that allowsdefining and practising with e-learning exercises for event driven process chainmodelling. That is a representative for graphical modelling techniques. The tool couldautomatically mark the students solutions by using temporal logic and model checking[3].THEORETICAL BACKGROUNDThe process of designing a database begins with an analysis of the kind ofinformation the database should hold and the relationships among components of thatinformation. The first stage is to formulate ideas about the information that will bemodelled and render them in the E/R model. Then, the abstract E/R design is convertedto a schema in the data-specification language of some DBMS. Most commonly, thisDBMS uses the relational model. If so, then by a fairly mechanical process the abstract- 214 -
e-Learning09design is converted to a concrete, relational design, called a relational databaseschema. Fig. 1 suggests how the E/R and relational models are used in databasedesign.
Turn each entity set into a relation with the same set of attributes, and Replace a relationship by a relation whose attributes are the keys for theconnected entity sets.o For each entity set involved in relationship R, we take its key attribute orattributes as a part of schema of the relation for R.o If the relationship has attributes, then they are also attributes of relation R.If one entity set is involved several times in a relationship, in different roles, thenits key attributes each appear as many times as the number of roles[4].DESIGN OF THE DATA MODELLING TOOLThe data modelling tool is designed to offer simple and intuitive interface to thelearner. The tool is used on a Databases course and its aim is to illustrate thetransformation process of the two diagrams. The functionalities of the educational unitare presented on figure 2.The learner could represent an entity by drawing a rectangle; an attribute bydrawing an ellipse and a relationship with a rhomb. A line denotes the type of therelationship between these E/R elements. The correctness of a drawn E/R diagram(ERD) is validated during the generation of relational diagram (RD).The learner could also save the drawn ERD and open it later. The saved fileextension is .dat, and at this stage of implementation these files could not be opened byanother data modelling tool or text editor.After drawing the ERD or opening it, the learner could generate the RD. The tooldraws the RD without any interaction of the user by analogy with ERD.
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e-Learning09Draw Rectangle<<includes>>usesSave
Draw Ellipseuses
usesOpen ERD
Draw ERDuses
Generate RD
Draw Romb
<<includes>><<includes>><<includes>>Draw RD
Validate ERD
Draw Line
Variable Name
Schema Type
Relational schema
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e-Learning09IMPLEMENTATION OF THE DATA MODELLING TOOLThe data modelling tool is experimental and its educational aim is to demonstratethe transformation of an ERD into a RD. A user interface is presented on fig. 4 and fig 5.It is divided into three sections. The learner could design the ERD at the upper rightfield. The upper left field shows to the user the ERD elements title and type. Thisinformation will help him/her to follow the designing process and also correct or deleteits elements. The result of transformation of the ERD is visualized on the section below.
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e-Learning09The learning unit offers two functional toolbars horizontally designed. There aretwo menus at the first toolbar: File and Schema. The File menu consists of the followingfunctions: New, Save and Open, which relate to the ERD. The other toolbar consists offive buttons: Move, Rectangle, Ellipse, Rhomb and Line. To draw one of these shapes,we should choose it and then point where to put the element in the upper right field. Thenext step is to write a title of the element in a pop-up window, which appears afterdrawing the element (fig. 4).After designing the ERD, the learner could save it in a file or generate therelational schema. To do the last operation, he/she should choose from the Schemamenu the option Generate. The view of the two diagrams is presented on fig. 5.CONCLUSIONS AND FUTURE WORKAn algorithm for transformation of an E/R diagram into a relational schema isproposed. Its educational purpose is to visualize the theoretical rules. The algorithm isused for the implementation of program modules which are contained in the datamodelling tool.REFERENCES[1] Chen, P. The Entity-Relationship Model: Toward a Unified View of Data. isACM on Database Systems, Vol. 1, No. 1, March, 1976.[2] Ivanov, S., J. Peneva. Distance Learning Courses in Computer Science Initiation and Design. Proceedings of CompSysTech2007, Ruse, 14-15 June, 2007.[3] Markus, S., R. Lackes, C. Brgermann. Using Model Checking toAutomatically Mark Graphical E-Learning Exercises. In Proceedings of WorldConference on Educational Multimedia, Hypermedia and Telecommunications.Chesapeake, 2008 (pp. 5302-5307).[4] Molina, H., J. Ullman, and J. Widom. Database Systems: The CompleteBook. USA, Prentice Hall, New Jersey, 2002.[5] Prados, F, I. Boada, J. Soler, J. Poch. A Web-Based Tool for EntityRelationship Modeling. International Conference on Computational Science and itsApplications, Glasgow ,2006.[6] Avolution.http://www.avolution.com.au/products.html(Lastaccessed15.07.2009).[7] Data modeling from CA. http://www.ca.com/us/data-modeling.aspx#overview(Last accessed 15.07.2009).[8] DeZignforDatabasesDatabaseModelingTool.http://www.datanamic.com/dezign/index.html (Last accessed 15.07.2009).[9] SmartDraw The Worlds Most Popular Business Graphics Software.http://www.smartdraw.com/ (Last accessed 15.07.2009).ABOUT THE AUTHORElitsa Arsova, PhD Student, Dept. of Computer Systems and Technologies,University of Ruse, Phone: +359 (0)82 888 827, -mail: firstname.lastname@example.com work was partially supported by the European Social Fund and the BulgarianMinistry of Education and Science under the Operation programme Human ResourcesDevelopment, Grant# BG051PO001/3.3-02/7.
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1. INTRODUCTIONNowadays many people exchange personal and business information via theInternet. They would like to be sure that electronic documents, which they send andreceive have been successfully protected against unallowed access and modification. Itis important to bear in mind also that all business activities require in addition strongauthentication of the users. Authentication could guarantee the content of an electronicdocument (e-document) and prove its origin, as well. Asymmetric cryptographicalgorithms have been widely applied last twenty years in different methods andschemes for digital signature generation. Many of them Fiat-Shamir, Schnorr,ElGamal, Rabin and Merkle, were designed especially for this purpose. One of the mostefficient asymmetric algorithms is RSA, which supports not only key pair generation, butencryption using public key and digital signature generation with message recovery.Another well known and wide spread asymmetric algorithm DSA, which is designedonly for the purposes of digital signature generation, was proposed in DSS [1]. Itimplements a scheme, based on hashing algorithm SHA-1, where the result is digitalsignature with appendix and the reverse procedure verification requires the original edocument as input. Recently public key standards recommend implementation of hashalgorithms- MD5 and SHA-1 in RSA-signature systems in order to prove data integrityby hashing first and then sign the hash value using senders secret key to generatedigital signature. As a result all digital signature schemes that use one-way hashalgorithm could be considered as reasonably robust and stable to cryptanalytic attacks.On the base of DSA and elliptic curve theory, Elliptic Curve Digital Signature Algorithm(ECDSA) was proposed. These three algorithms, mentioned above RSA, DSA andECDSA have been recommended by Bulgarian low of electronic document and digitalsignature, which came into force in 2001.Asymmetric cryptographic algorithm could be applied not only for digital signaturesgeneration, but in different authentication schemes, based on the principles of checkingwhat the user is allowed to propose, proving that he is the right person. For example, itcould be something he knows passwords, PIN-codes, secret parameters forhandshaking, etc. or something he has obtained to take part in a session accessticket, secret cryptographic key, which is used to generate digital signature or just aparameter for one-way recognition. All methods, algorithms, standards and protocols,used for strong authentication, data integrity and no repudiation by digital signatures onthe base of key pair (public/secret) define the subject of public key cryptography [3,4].The paper discusses basic valuable security services that public keycryptosystems provide digital signature generation and verification, authenticationprotocols, notarization of e-document. An educational system, which presents differentprotocols and methods applicable in e-business operations, is proposed. The main goalis to give students theoretical and practical knowledge in public key cryptosystems, theirimplementation in electronic document processing and authentication schemes.- 220 -
e-Learning092. RSA-ALGORITHM KEY GENERATION AND ENCRYPTIONThree researchers Rivest, Shamir and Adleman first published details of theRSA-algorithm in 1978 [2] as a result of their efforts and towards minimizing keycomplexity of ciphers and their number. For example, if a symmetric cryptosystems hasn users, the necessity keys are on the order of n2 in comparison with 2n, required inasymmetric cryptosystem, proposed by the authors.RSA-algorithm could be explained by 6 steps as shown in fig. 1 where steps 1-4present RSA-mathematics, step 5 encryption of a plain text P and step 6 decryptionof the cryptogram C.Step (1): The user A chooses two large primes p and q. They are secret parameters.Step (2): A multiplies p and q. The result is N : N = p.q .Step (3): A calculates the Oilier function (N ) : ( N ) = ( p 1).(q 1) .Step (4): A generates his own secret key SK A : 0 < SK A < ( N ) and SK A relativelyprime to ( N ) .A calculates his own public key PKA as multiplicative inversion, i.e.PK A = SK A1 mod ( N ) .The problem of modular inverse sometimes has a solution, sometimes not. Ingeneral it might have right solution only when ( N ) and SK are relativelyprime, otherwise there is no solution. When ( N ) is a prime, there is theonly one inverse value in that range.Step (5): The user A shares his public key PKA and N with the user B. If the plain text isPKP, the user B can encrypt it as follows: P A mod N = C .Step (6): The user A receives the cryptogram C from the user B. He is the only personwho can calculate the plain text P from C, because only he knows the key SKhis own secret key SK A . He calculates P as follows: P = C A mod N .Fig.1 RSA-algorithm Key Generation3. DIGITAL SIGNATURES ENABLE E-DOCUMENT AUTHENTICATIONDigital signature schemes could be divided into two groups depending onrequirements of the secret and public parameters and complexity of two procedures generation and verification.The first group includes systems based on digital signature with appendix, wherean e-document should be cryptographically processed by the secret key of the senderafter hashing. At the same time digital signature of one and the same e-documentchanges, because except hash function, generation process uses an additional secretparameter with different value when this document must be signed several times.Typical representatives of this group are algorithms ElGamal, Schnorr and DSA.The second type of digital signature systems is based on message recovery. Thismechanism is used by algorithms of Rabin, Nyberg-Rueppel and RSA. They allowverifying digital signature using only obtained signature and the public key of thesender. When a hash algorithm is used SHA-1 for example, it is able to process plain64text of 2 bits to hash code of 160 bits. This is the reason to apply RSA with hashalgorithm ensuring short hash codes of e-documents. As a result representatives of thisgroup could propose invulnerable to attacks digital signature schemes.Fig. 2 presents main steps of DSA, digital signature generation and verification.
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e-Learning09Step (1): User A chooses two large primes numbers2 L 1 < p < 2 L , where 512 L 1024 ; 2 159 < q < 2 160 ; q divides evenly (p-1).p 1q
Step (2): User A chooses a number h, smaller than (p-1) and computes g = h mod p .Step (3): User A chooses a random number, which is his secret key SK A : 0 < SK A < q .Step (4): User A calculates his public key PK A : PK A = g SK mod p .A
r = ( g k mod p ) mod q ;s = (k 1 (h( P ) + r.SK A )) mod q .User A sends his message P to user B with digital signature as appendix, asfollows: ( P, r , s )Digital signature verificationStep (3): User B verifies the signature. For this purpose he calculates four parameters:u = s 1 mod q ;v = (h( P).u ) mod q ;w = (r.u ) mod q ;
e-Learning09not able to read the information in it. In this way, applying the protocol for blindsignature, the process of authentication could be coordinated by the Notary, because heis the only person, who can prove the signature of each e-document.4. PRACTICAL KNOWLEDGE IN PUBLIC KEY ALGORITHMSFor the purposes of education in public key cryptography a learning system isdesigned. It deals with two asymmetric cryptographic algorithms RSA and DSA,presenting their mathematics and main steps for key pair signature. RSA is used toexplain group and blind signature. The first task is to generate primes. Every studentcan choose how to get two primes using a module of the educational system designedon the base of Rabin-Miller algorithm or entering his own code. After successfully primegeneration, the rest parameters N and (N ) must be calculated. The second task iskey pair generation. Finally all parameters public and secret, should be checked bythe RSA-control module of the system. If they are correct the student will receivemessage that confirms successfully done RSA scheme, otherwise he should try toreveal mistakes and propose new values, for p and q and calculates SK and PK. Thesystem proposes RSA-module for key generation which could be used by users whohave no good skills in programming. They can access, as well, samples with six stepsof RSA-algorithm and their results, as shown in fig. 3.
p = 1733
p = 17
q = 2347
q = 23N = 391
E PK ( P) = P mod 4067351 = CDSK (C ) = C 3145759 mod 4067351 = P
e-Learning09users insist on protecting their personal and business information from tampering andconfirm the authentity of every e-document at the same time. Contemporarytechnologies for timestamping employ notarization for indicating the time when it wasperformed.Fig. 4 presents functionality of the educational system. Every student can accesstwo main modules RSA and DSA. He can use text files, proposed by the system as edocuments to sign each one employing group or blind signature. Implementation of twohashing algorithms MD5 and SHA-1 allow to display differences between digitalsignatures of one e-document, obtained after signing its different hash values.
List ofpublic keys
Digital signaturegenerationDSAFig.4 Educational System in Public Key CryptographyHaving in mind programming skills of the students and their common knowledge inWeb-technologies main learning modules of the system are proposed using MicrosoftVisual Studio 2008, C++ program language. Students can access three panels,designed to present main cryptographic schemes, described above. The first paneldeals with implementation of RSA-algorithm for key pair generation. As shown in fig. 4,it is closely connected with group and blind signature modules, which could be appliedafter defining the cryptographic keys. The second panel is proposed for digital signaturegeneration using DSA. In order to learn more about hashing procedures applied inpublic key cryptography, students could choose the last panel. It proposes results hash values of e-documents obtained by MD5 or SHA-1.
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e-Learning095. CONCLUSIONThe educational system, proposed in this paper is applicable for e-learning incryptography and e-business. Students have opportunity to understand benefits thatpublic key cryptography could provide to business. They learn more about principles ofe-documents authentication. The system could be useful for users who would like tohave more knowledge about design and implementation of asymmetric cryptographicalgorithms in digital signature protocols.REFERENCES[1] The Digital Signature Standard (DSS). Communications of the ACM, v.35,1992, 7[2] Rivest, R., A. Shamir, A. Adleman, A Method for Obtaining Digital Signaturesand Public Key Cryptosystems. Communication of the ACM, v.21, 1978[3] RSA Technology Web Site (http://www.rsa.com)[4] Stallings, W. Network Security Essentials: Applications and Standards,Prentice-Hall, 2000.[5] Chaum, D. Blind Signature Systems, 1998.[6] Secure Hash Standard, NIST, 1995.[7] Mukesh, K. Public Key Cryptography With Matrices. Proceedings of the FifthAnnual IEEE SMC, 2004.ABOUT THE AUTHORAssoc. Prof. Irina Noninska, PhD, Department of Computer Systems, TechnicalUniversity Sofia, Bulgaria, Phone: +359 2 965 34 71, -mail: firstname.lastname@example.com.
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INTRODUCTIONUse of information and communication technologies has reached massiveexpansion recently. Computer users are to be found in every social and professionalgroup. Computers simplify a lot of work tasks, make methods of communication easierand more accessible, intermediate new information, etc. These modern technologiescan play a role of important supporting and compensational means for a group of userswith specific needs. It is mainly people with some mental and health problems who facedifficulties with communication, and common means of communication are usuallyunusable for them. Computers can act as a positive agent in such cases. However,hardware and software must be adjusted to the specific needs and it is not possible toimplement experience gained and applied with common groups of users.There is a potential group of users with specific disorders who could make use ofthe development of communication skills. E.g. people with autistic spectrum disorders,Down syndrome, forms of mental retardation and other development defects whichcause problems to learn and use spoken language, written text, and other commonlyused means of communication. Various methodics bringing interesting results havebeen worked out to develop communication skills of people with the above mentioneddisorders. For instance, such methodics is the system PECS (The Picture ExchangeCommunication System) and its Czech variation VOKS. The base is a use of picturesrepresenting particular concepts, things, activities. The user gradually becomes familiarwith new concepts pictures, which are then incorporated in their list. Choosingpictures and placing them on a sentence strip helps them learn to create even simplesentences. [3]In 1998, the Department of informatics and computers, the University of Ostrava,decided to help interested people with a health handicap. The first stage was to supportthe study of people with hearing impairment; the department introduced a distant formof studies in the bachelor programme Applied Informatics. The distant form of studiesenabled the deaf applicants to join the study. The distant form and study fields proved tobe interesting even for applicants without health handicaps and currently there is a greatconcern over both distant and combined form. The university support of the study forapplicants with health impairments is still being developed, e.g. in the area of projectsfocused on support of health handicapped students, special educational courses,development of accessible web, and recently in cooperation with special schools in thedevelopment of communication software for children with specific disorders.METODICS FOR LEARNINGBased on the requirements from special school, we are developing an applicationwhich should facilitate communication with a child with specific disorders such asautism, Down syndrome, mental retardation and others. Autism is a mental handicapwhich demonstrates in worsened communication skills. Speech formation of people with- 226 -
e-Learning09an autistic spectrum disorder is delayed and some of them do not speak all their life orthey use only limited number of words, frequently corrupted. Several methodics toimprove their communication skills have been developed. [10]One of them is the PECS methodics created in 1985. It is an alternative educationand communication method for communication between an adult and a childhandicapped by autism, mental retardation, and other specific disorders. [11] Thefundamental principle is an exchange of a picture representing a particular thing for areal one. Continuous practice leads the child to learn how to use concepts correctly andimprove its communication skills. The whole process is emphasised by stimulation. Forinstance, the child gets a chocolate if it brings the correct picture. Follow-up educationleads not only to knowledge of pictures concepts, but also to composing a shortsentence from the pictures. The last phase of education encourages the children tocomment independently the action around them and to answer direct questions. Thesystem also helps some pre-school children to develop speech. An elaborate methodicsof work with a system provides six training phases with examples, manuals, and advice[1] [2].The Czech system VOKS mainly stems from the methodics and principles ofPECS. However, it brings some improvements and it tries to adjust to particularities ofthe Czech language. Unlike PECS, it emphasises visual support of speech of bothcommunicating partners. Thus there are changes to basic situations in all lessons. Themethodics is divided into two basic parts. The first contains important informationconcerning teachers of the VOKS system and training environment. In addition, itdescribes preparation of individual tools for communication and the way of rewardchoice before the whole training of the communication begins. The second part containseducative lessons, which form the backbone of the education. The client learns to askspontaneously for a favourite thing in exchange for a picture, to go on his own to thesymbol container to get a picture and then to hand it in subsequently to the partner.They learn to ask different people about anything in an unfamiliar environment, tochoose appropriate symbol from several pictures, to complete correctly a simplesentence on a sentence strip using pictures and then to ask by help of the sentencestrip. Finally, they learn to react to various questions and to comment on theenvironment around them. The supplementary lessons then develop picture inventoryand syntax [6].Experience with this system proves that the children evidently improved theircommunication skills, as well as those who did not have any communication skillsbefore application of the VOKS system. A number of children faced other problem,though. The users started to handle more concepts pictures. Their container book,where the pictures are attached by velcro, became very bulky and physical manipulationgot difficult for the children. With respect to this fact, there was a requirement defined todevelop a software application in order to solve these problems. There are severalprogrammes for the PECS system, e.g. Boardmaker [12]. Yet, this does not meet thechosen methodics, moreover its cost (300$ up) is high. Another similar application isOverboard. Applications usually contain several thousands of colour or black-and-whitepictures, which can be chosen from various categories. However, the educationprogress and work with the programme is not focused a particular methodics and thusthere is a higher need of teachers technique [8].When developing and implementing for users with specific disorders, it is usuallynecessary to use other procedures and methods than with common applications. Thetarget group requires totally different approach taking into consideration their health ormental handicap.The most striking difference is in the programme control. Embedded standardsand procedures are often inappropriate or unusable. Users with specific disorders oftencannot use common control mechanisms which are being used in applications of- 227 -
e-Learning09contemporary operating systems. Their creators try to build up work area of a specificapplication as large as possible, but control features (close, minimize and maximize,menu offer, etc.) are as small as possible.APPLICATION VOKSWhen designing a new application environment, it was necessary to get rid of allcontrol features which were not directly related to the main use of the programme, andwhich could lead to early closing, new configuration, initialisation of new actions, etc. Alot of children find difficult to concentrate on realizing the needed performance. Theyfrequently unwillingly click the mouse or wander the cursor round the screen. Thus it ishighly important that children could not consciously or by mistake initiate functionsindirectly related to the course of the programme.The application control must not be demanding, unclear, or complicated at all.Generally common activities (such as double-click on a mouse) are almost impossiblefor numerous users with specific disorders. The control features of the programme mustbe conveniently large and their start-up and control as easy as possible. An ideal casewould be if the application, having been opened, took up the whole screen of thedesktop, being maximized all the time with no possibility of any change. The use of thementioned software counts with the use of special hardware to control the computer.Users with motor handicap are e.g. supplied with IntegraSwitch, which works as anaspiration-expiration switch. Classical mice and keyboards are replaced by alternativepositioning devices and keyboards, such as BickTrack, KidTrack, Roller Joyistick,BigKeys, IntelliKeys, various sensor buttons, and others. The main advantages arelarger control features and more robust construction. On the other hand, thedisadvantage is usually the price which exceeds several times the price of commonlyused hardware. Purchase of such special devices can become unaffordable for manyfamilies with handicapped children. The solution could be use of touch screens, e.g. innew types of computers generally called nettops. It is a kind of cheaper computerscontaining all parts in one case together with a touch screen. The user can control theapplications directly on the touch screen, which seems to be more convenient especiallyfor children with specific disorders than using mouse or other positioning devices. Somechildren have motor problems when using mouse, moreover they have problems withmoving the mouse while concentrating on another place - on the screen. Nevertheless,despite all of the above mentioned special hardware tools, it is convenient to create thedeveloped software in a way to be usable even on common desktops, or notebooks withtheir usual peripherals.An application for children with specific disorders requires two basic parts. The firstone a functional part designed for children. The second serves to their parents orhealth workers for configuration and setting. With respect to diverse requirements of thetarget users, it is necessary to enable clear, but sufficiently complex and extensivesetting offers. Unlike common applications, requirements on functionality and look of anapplication for particular individuals are often very distinct and influenced by health ormental problems of the child.With respect to the fact that the programme VOKS and similar teach the user tocommunicate, they act as a learning software in the first phase. The application tries touse the VOKS methodics and it contains several levels and phases of usage fullycorresponding to the chosen methodics.
Level one the user learns to choose the picture by clicking. The desktop showsonly empty fields and one picture which has been randomly chosen from a predefined group of pictures concepts. The childs task is to click on the picture. Theprogramme monitors all clicks in the empty fields and the information is saved inan XML file. During the development and verification of the application, we have- 228 -
found out that it is not convenient to use classical way of control and distinguishclicks by left or right button. Children with certain disorders find work with mouseitself motorically very demanding, so the button distinction was rejected. Addingsound to the picture proved to be an advantage, though. Support of othersensation makes the process of learning easier.Level two the user sees two pictures. One represents positive, pleasantemotions and feelings, the other negative. The child learns to choose the pictureconnected with positive emotions.Level three having mastered the two previous levels, the vocabulary of the usersexpands and individual concepts pictures are classed in categories, which differin colour. The child learns to choose concepts from the categories and places thepictures onto a sentence strip.Level four serves for independent communication and the users composes shortsentences and sequences using concepts from different categories. At first, thesentence strip enables the child to form simple sentences such as I want achocolate, etc.Level five increases communication level and enables to create a picture,description of events and situations.
The first three phases of programme usage primarily serve to learn how to controlthe programme and to get acquainted with the pictures and their content. It is highlyimportant to observe activities of the user. This happens on two levels. The first isobservation of the parent, tutor, or health worker who teaches the child how to use theprogramme. The second is built-in monitoring in the programme itself. Each activity isstored in the XML data file format. Subsequently, it is possible to retrieve informationabout which pictures are difficult for the user to be understood, which are not masteredso well by the users, which position on the screen is problematic to reach, etc. Thisinformation can serve not only to the tutors, but also to the creators of the programmefor eventual improvements.
e-Learning09special school and to adjust the interface and functionality of the application. The needto monitor children in their usual activity showed to be essential. Such experiencegained by monitoring of the children was important for the creators of the programme.When developing an application for handicapped users, the creators cannot fully rely ontheir previous experience. Due to the fact that application developers do not have adirect contact with children with autistic spectrum disorders, Down syndrome, mentaldisorders, and other very specific disorders, it is important to get familiar with practicalreactions and behaviour of such children.The situation is then complicated by individual ways of behaviour, which relates tothe extent and level of the disorder.PROGRAMME TESTINGHand in hand with an increase in requirements and properties of software tools,there is a need of appropriate and profound testing. Despite the fact that the abovedescribed programme does not include any complicated functional structures oralgorithms, and work of complicated and expensive devices is independent of itsactivity, it is necessary to secure its high reliability. Children suffering from e.g. autismfind difficult to get used to a new environment and unfamiliar things. The time neededfor the child to accept the programme and learn to work with it is usually very long. Incase of serious disorders, there is a danger that the child will decline it or refuse to workwith it. During testing of an application by children, there should not be any fundamentaladjustments of the graphical interface and its control. Apart from classical procedures oftesting of the programme functionality, it is important to observe how the child mastersthe application. It is not enough to use only usual static and dynamic testing means,analysis of the source code, monitoring by testing programme, special tests on memoryusage or load, etc. [6]Thus the application incorporates internal mechanisms which monitor usersactivities. Monitoring data about manipulation with programme objects are stored inXML files. The application contains an interface designed purely for parents and healthworkers which enables to evaluate both childs skills to work with the programme and itsfunctionality. The output data then acts as a feedback for the creators of the programmeas well as for parents and health workers, who can adjust the process of educationmore effectively. Another way to verify the programme functionality is methods used inquality oriented pedagogical research. It primarily concerns the method of observation[4]. The children use the application together with the parents or health workers. Theadults act both as a pedagogue, who teaches the child to use and communicate withthe programme, and an observer, who check for reactions and skills of the child. Theobservation results are very important not only for the educational process, but also forthe development of the programme.Unlike commonly used software, there are specific problems when testing: The target group of users is not large enough. The target group of users is very diverse and the level of the disorder considerablyinfluences ability to work with the programme. It is necessary to take intoconsideration individual needs of the users and to enable more possibilities ofsetting and adjustment. The phase of learning how to use the programme is very time consuming. Thisresults in longer period of testing than with usual applications. Children with higherlevel of disorder can take months to pass from one phase of the programme toanother. It is important to largely cooperate with parents and health workers because thechildren find difficult to get used to changes in environment and unknown people.- 230 -
e-Learning09CONCLUSIONConcerning the fact that the target group of users of the described software is notvery large, forming a relatively small sample, it is advisable to offer it a quality solution.People with specific disorders are mostly dependent on supporting tools and means. Itappears that information and communication technologies offer a way to improve theirlife standard. They enable to create tools which were not possible when using commonapproaches or they were very difficult to realize. When creating software for childrenwith specific disorders such as autistic spectrum disorders, Down syndrome, mentalretardation, etc., it is necessary to cooperate with doctors, psychologists, and healthworkers. It is the area of programme control where the creators of programmes mustapply totally different criteria and processes than with common applications. Testing ofthe developed software is more complicated, time consuming, and apart from commoncode testing processes and application functionality, it also requires observation of itsusers.REFERENCES[1] Begeer S., Banerjee R., Lunenburg P. Brief Report: Self-Presentation ofChildren with Autism Spectrum Disorders, Journal of Autism and DevelopmentalDisorders, Volume 38, Number 6 / July, 2008, ISSN 1573-3432[2] Bondy S. A., Frost L. What is PECS?, http://www.pecs.com/WhatsPECS.htm[3] Charlop-Christy M.H., Carpenter M, LE L., LeBlanc L, Kelley K. Using thePicture Exchange Communication System (PECS) with children with autism:Assessment of PECS acquisition, speech, social-communicative behavior, and problembehaviors. Journal of Applied Behavior Analysis, 35, 213-231, 2002,http://seab.envmed.rochester.edu/jaba/articles/2002/jaba-35-03-0213.pdf\[4] Gavora P. vod do pedagogickho vzkumu, Paido, Brno 2000, ISBN 8085931-796[5] Hoekstra R. A., Bartels M., Cath C. D., Boomsma D. I. Factor Structure,Reliability and Criterion Validity of the Autism-Spectrum Quotient (AQ): A Study in DutchPopulation and Patient Groups, Journal of Autism and Developmental Disorders,Volume38,Number8/September,2008,ISSN1573-3432,http://www.springerlink.com/content/m184028q8305/?sortorder=asc&p_o=10[6] Knapcov M. Vmnn obrzkov komunikan systm - VOKS. Praha: Institutpedagogiko psychologickho poradenstv R, 2005, ISBN 80 86856 07 0[7] Lacko B.: Testovac prostedky a testovac postupy, Tvorba software 2006,Ostrava 2006, ISBN 80-248-1082-4[8] Scherz J., Meng-JU T., Broston S. Adult Preferences Between Two SymbolSets: Comparing Boardmaker and Overboard, Wichita State University 2006,http://convention.asha.org/2006/handouts/855_1293Scherz_Julie_073114_111306111037.pdf[9] Yoder P., Stone W.L. A randomized comparison of the effect of twoprelinguistic communication interventions on the acquisition of spoken communication inpreschoolers with ASD. Journal of Speech, Language, and Hearing Research, 49, 698711, 2006.[10] http://en.wikipedia.org/wiki/Autism[11] http://www.pecs-usa.com/[12] http://www.mayer-johnson.com/ProdDesc.aspx?SKU=M125ABOUT THE AUTHORRostislav Fojtik, Department of Computer Science, Faculty of Science, Universityof Ostrava, Czech Republic, 30.dubna 22, Ostrava,Phone: +420597092173, E-mail: firstname.lastname@example.com- 231 -
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INTRODUCTIONEducation as a process of acquiring knowledge is an interesting subject, studiedby many experts from different fields. The actual organization of the educationalprocess with the aim to increase its efficiency is a topic on which it is being worked allthe time.In [2,4] it was mentioned that the level of the student training is changingcontinuously. It influences the quality of the activities each person does after the specificknowledge assimilation. Therefore it is useful to evaluate the knowledge dynamic of theseparate terms, topics and school subjects.This is done using different characteristics, for example intensity of knowledgeassimilation, flow of the school material, knowledge restoring, probable vector of termassimilation of the school subjects and so on.MODELLING DYNAMIC OF THE KNOWLEDGEUnder the knowledge dynamic description can be understood a set of a finalnumber of assimilation, control and knowledge diagnostic levels stages. The differentterms from the school subjects or the different kinds of activities pass through thesestages in a different sequence.In the theory three different kinds of nets are differentiated: opened, closed andmixed. In the opened nets the input stream of the school material doesnt depend on thenet state or the number of the terms entered in it yet. In the closed nets the number ofthe terms in each of them is a constant, and the intensity of the school material streamon the entrance of each stage depends on the system status. The differentiation onthese stages can be done easily with the introduction of different initial markings in Petrinets. Essentially, Petri net machine has powerful means for describing the dynamic of asystem with parallel asynchrony process. So, it can be used for modeling the dynamicof knowledge assimilation in the school process. In the other hand excepting the parallelassimilation of different knowledge, there is a parallel regarding to the two mostimportant processes: assimilation and forgetting of the knowledge (its normal becausein the nature each action has a corresponding reaction).The introduction of Petri nets can be graphical or analytical. The graphicalintroduction is easy for a visual acquire, and the analytical, by the vectors and matrixes,has convenient means, by software processing, for analyze and syntheses. Whendescribing a system structure by Petri nets two structure elements are used:transactions, giving the actions in the system, and positions, giving the conditions fortheir actions execution and the result of them. So, for each of the transition can bedefined input and output positions. In the context of human perceptions, this is apresentation of the causality-consequently relations. The action of the cause marks withmarkers in the corresponding input places. The system static is modeling by a graph.- 235 -
e-Learning09When the action, modeled by this transaction, is activated and executed the markersfrom his input places pass to his output places. Thats why in the theory is said, that thenets dynamic is modeling by the accessibility tree. It shows all accessibility markings ofall positions executing all transitions. Consequently, the net dynamic is defined by therules for the transaction execution and the initial system status.Building a states graph allows quick and easy determination of the transitionsequence, which will guide the educational system from one state into another. Forbuilding the graph a variety of software products can be used DPN tool, INA,Matlab_toolbox PNet, etc. Searching for the fastest route between two states in thenetwork can be optimized using those products, as well as simulation of the wholenetwork using PIPE. Analyzing the already built graph can lead to conclusions, aboutwhat actions must not be taken in order for the system to remain in a desired state.TWO BASIC KINDS OF NETS CLOSED AND OPENEDIn the most simple version of the closed net the student pass two stages of a termassimilation: assimilation stage S2 and forgotten stage S1 /fig.1./. With b1 is noted themean time for forgetting the term and with b2 the mean time for assimilation orrecovering the term knowledge. D1 and D2 are respectively the actions forforgetting/recovering the terms.D1D2b2
Fig.1.The introduction of this scheme with Petri net with these two basic processes: ofassimilation and forgetting, with their corresponding transitions t1 and t2 is given onTable 1.We use a simple way to present a Petri net with a table: the rows associate to thetransitions, the columns associate to the position. For each position we show input andoutput number of arcs and the initial state as a number.Table 1P1P2P3P41110inoutinoutinoutinoutT111111001T200000110The necessary preliminary conditions for the assimilation process to beaccomplished are: the presence of a student p1, knowledge for the current terms inthe stream of school material p2 and the absence of this knowledge in the studentknowledge p3. After this stage as an output condition presence of knowledge for thisterm in the student knowledge is obtained - p4.One more complicated version is when the student has to assimilate N numbersof terms. The net, which describes the dynamic of this knowledge with a curtain numberof compulsory terms, can be with different complexity. Again the most simple caseincludes two stages: stage of assimilation S2 and stage of forgetting S1. Here with b1iis noted the mean time for forgetting of the i-term, and with b2 the mean time forassimilation or recovering of the same. D1 and D2 are respectively the actions onforgetting/recovering of the terms. O2 presents the sequence of the terms on theentrance of the assimilation stage S2 /fig.2/.- 236 -
Fig.2.For simplification is accepted b1i= b1=const. 1=1/ b1 is the intensity of forgetting ofthe separate terms. In this model can be accounted the terms which must be finallyassimilated with probability p22. With this probability the term is arranged in the end ofthe queue O2. And with a probability p21 the assimilated term passes in the stage S1.In the model of figure.2. is not enough detailed the process of control (diagnostic)of the knowledge and the following assimilation. This process is reflected only with thesummary stage S2. As an example for more adequate net model securing guaranteedquality of specialist training can be defined as following: The stage S1 corresponds to Nbasic states, allowing constant intensity of their forgetting, equal to 1i= = 1/bi (i=1,N).The stage S2 with queue 2 reflects the process of knowledge control with mean timefor control od a state, equal to b2. The accepted by the control as unassimilatedpositions, from the exit of stage S2 go with possibility p23 in the assimilating stage S3.The positions which are accepted as assimilated on the stage S2 go to S1 withpossibility p21. Unassimilated positions from the exit of a stage S3 return with apossibility p33 in the queue 3 to continue assimilating (each assimilation can be dividedon phases). The positions from stage S3 , with possibility p34 , pass to the next S4,stage with queue for control or diagnostics of the knowledge 4 and mean time forcontrol b4. From the exit of stage S4 the positions accepted as unassimilated return tostage S3 with possibility p43. Accepted as assimilated positions, with possibility p41return to stage S1.In the general case, for the multistaged net for assimilating knowledge the intensityof assimilating, control, diagnostics and other kinds of knowledge processing on stageSi , can depend on the number of the positions ni ( i = 1, N), which are located on acertain stage.On this base there can be design two-priorited and two-staged closed models.Details about the reasons and the schemes of the models are given in [3].In the most simple version of the opened net the student goes through two stages:stage of assimilation S1 and stage of control S2 /fig.3/. D1 and D2 are respectively theactions on assimilation and control of the terms.
D2 S2b1
Fig.3.Modelling with the Petri nets there can be separated two stages with threetransitions: t1 process of assimilating, t2 process of controlling and letting thestudent work with the system and t3 process of control and returning the student foradditional training /Table.2/: The necessary preliminary conditions for assimilatingprocess to be done are: presence of a student p1, knowledge for the current term in- 237 -
e-Learning09the stream of the school material p2 and not full knowledge of the term in the studentsknowledge p3. After this stage as an exit condition there is obtained presence of somekind of knowledge about this term in the students knowledge - p4. The necessarypreliminary conditions for control and letting the student work are: presence of a teacher p5, presence of some kind of knowledge about this term in the students knowledge p4 and judgement for satisfying students knowledge p6. After this stage as an exitcondition turns out the end of the training p8. The necessary preliminary conditions forcontrol and returning the student for extra training are: presence of a teacher p5,presence of some kind of knowledge about this term in the students knowledge - p4 andjudgement for non-satisfying students knowledge p7. After this stage the exit resultindicates not full knowledge of the term in the students knowledge p3.Table 2P1P2P3P4P5P6P7P811101100in outin outin outin outin outin outin outin outT11111100100000000T20000001011110001T30000011011001100CONCLUSIONSThe advantage of using the Petri network engine is the possibility to analyze andresearch the systems behavior.From the designed Petri net models of the different systems, presenting thedynamic of knowledge assimilating there is confirmed that this machine has powerfulmeans of modelling like Turing machines, but in contrast to them it contains biggerdeciding power typical for the finite automats. There are not so much languages thatallow all terms, constructing a topic, and all topics constructing a school subject, to bedescribed in most details. Besides, all the time from the training process differentpriorities of the obligated and additional knowledge are given and controlling if thenecessary assimilation power is reached. In addition, through all the assimilationprocess time the natural possibility of the student to forget is accounted.REFERENCES[1] Filipova, K., Sv. Stefanova, Defining the Performances of the KnowledgeDynamic Models, Annual School.Lectures- 2003, 3-5, vol.23, number 1, Sept2001/2003.[2] Filipova, K., Sv. Stefanova, Net Models of Knowledge Dynamic, AnnualSchool.Lectures- 200,; 29-32, vol.23, number 1, Sept 2001/2003.[3] Stefanova, Sv., K. Filipova, I. Zheliazkova, A Technology for Teaching byMeans of an Intelligent Animation Environment, Annual School.Lectures- 2003, 17-19,vol.23, number 1, Sept 2001/2003.[4] Sviridov A.P., I.A. Shalobina, Net models of knowledge dynamic, Moskva, MEI,1992.[5] K., . , , First International Conf. Challenges in HigherEducation in the 21st Century, pp.48-50, may, Sozopol, 2003.ABOUT THE AUTHORSKrasimira Filipova, PhD, Department of Automatic, University of Sofia, Phone:+359 02 9652526, Email: email@example.com Stefanova, PhD, Department of Computer Systems, University ofRousse, Phone:+359 82 888 356, Email: email@example.com- 238 -
INTRODUCTIONToday most universities offer distance programs using a variety of on-line methodsand learning management systems (LMS) are often used for distribution of material andcommunication. It is important for distance teachers to be aware of the technologicaldevelopment in recent years and how the technology can be used to build up asupportive learning environment that encourages students learning, they must beaware of what they can use and how they can use it. Believes and attitudes areimportant in this context and research has shown that teachers attitudes towardcomputers influence their computer use [1, 2], although Sadik [3] argues that moreresearch is needed in order to improve our understanding of the relationship betweenattitudes towards computers and their use and how it influences computer use inschools.Distance education today is built on a systematic and intensive use of technologythat does not only call for good computer skills and knowledge of how the technologycan service the distance teaching goals, but also positive attitudes and interest in usingthe technology. Li [4] came to the conclusion from a research of 15 secondarymathematics and science teachers and 450 secondary students, that the students andthe teachers attitudes were almost always polarized, where the students were morepositive than the teachers and wanted more use of technology.The importance of using multiple tools in distance education has often beenconfirmed by research, especially the use of synchronous and asynchronoustechnology [5]. The distance teachers role is in a way the same as other teachers, butin stead of face-to-face communication, the distance teachers use tools like LMS todistribute learning material and assignments, communicate with students and givefeedback. This is important because distance teachers meet their students rarely oreven not at all, so the on-campus communication and discussion has to besupplemented by other means. It is therefore important to understand how distanceteachers use the technology available to them and also to understand their beliefs andattitudes, in order to support them to develop good quality distance education.Distance education has grown fast in higher education in Iceland, from 1997 to2008 the number of distance students rose from 307 to 3.295 [6, 7]. The programs aredifferent in each university, some universities are only offering single distance courseswhile others are offering full programs, that the can end with degrees. The organisationof the distance learning programmes is also different, in some programs the studentsmust attend on-campus sessions, but in others it is not obligatory and someprogrammes even do not include on-campus meetings. Often the teachers must design- 239 -
e-Learning09and prepare e-material for the distance students that may consist of recorded lectures,slide presentations, video clips, interactive assignments and files in different formats.This paper describes a survey conducted at Reykjavik University (RU) in Iceland,examining the attitudes and believes among the distance teachers. RU has twodistance programs, Diploma in Technology (90 ECTS) and Diploma in AppliedComputing (120 ECTS), and uses its own LMS called MySchool. The distance teachersare encouraged to be active in their distance teaching and are offered support, bothtechnical and pedagogical. They have to make their own e-material and a typical ematerial is a recording of a slide show with the teachers explanations. The student cansee what is on the teachers screen and listen to his/her voice but he/she does not seethe teacher. Of course there can be different software on the teachers screen and theteachers can use special monitors to write or draw on to explain further. In addition tothat the teachers prepare a variety of material in different formats for the distancestudents. The distance teachers have the opportunity to meet with students once ortwice each term, when the distance students are invited to come on campus, but it is notobligatory for the distance students to attend those meetings.METHODThe study was conducted in the autumn 2008 at Reykjavik University (RU) inIceland. The participants were distance teachers in two diploma courses at RU,Technology and Applied Computing. The response rate was 70% or 21 answers,females were 3 (14%) of the respondents and males 18 (86%).An especially designed questionnaire based on the authors previous research [8,9, 10] was used in the study. The questionnaire consists of three background questions,two questions about communication, one multi-item question about the use of differentonline options, rated on a five point scale, and one multi-item question about theattitudes towards distance teaching, also rated on a five point scale. In the end therewas an open question for the participants to give their opinion on distance education. Aspecial software for interactive data collection, Outcome (www.outcome.is), was usedand a request to participate was sent to teachers by e-mail. The study was anonymousand there was no possibility of tracing individual answers.RESULTSJust over half (13, 59%) of the distance teachers were 51 years or older, 5 (22%)were between 40 and 50 years old, and 3 (14%) under 40. Most (18, 86%) of themclaimed that they had good preparation for using computers in distance teaching.The distance teachers were asked about their attitudes toward distance educationand most (18, 86%) of them found distance education a realistic alternative in Icelandiceducation and 17 (81%) agreed that everyone can study by distance education if theyare patient and interested as table 1 shows. About half (12, 57%) of the distanceteachers said they wanted more training in distance teaching and only 1 (5%)disagreed. Just over half (11, 52%) of them agreed that with time and training it will beas convenient to teach at distance as on-campus, but they were not all convinced as29% (6) disagreed to that statement.Table 1. Distance teachers attitudes toward distance education
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Agree or totallyagreeN (%)
NeutralN (%)
Disagree ortotallydisagreeN (%)
The distance teachers were asked what on-line options they used for theirdistance teaching and all of them (21,100%) reported using the schools LMS alwaysor often and most (20, 95%) of them claimed they always used PowerPoint slides.Nearly all of the teachers (20, 95%) said they expected students to hand in theirassignments online and almost all (17, 81%) of them recorded material for theirstudents. Online discussion was used by half (12, 57%) of them, but only 2 (10%)claimed it was a part of the grading to take part in online discussion.When asked about other options 5 (29%) claimed they used e-mail always oroften to distribute material and 3 (26%) graded students assignments online as figure 1shows.
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Agree ortotally agreeN (%)
Table 3 describes the distance teachers attitudes towards distance students andcommunication. All (21, 100%) of them agreed that distance teachers must answerstudents quickly and without fail, but only half (11, 52%) agreed that the time limitsshould be less than 24 hours. Almost all (20, 95%) agreed that it is important to meetdistance students face-to-face and over half (12, 57%) disagreed that they have morecontrol over students learning in distance teaching than in on-campus teaching.Table 3. Distance teachers attitudes towards distance students and communication
21 100%20 95%
Figure 2 shows the frequency of the distance teachers on-line communication withthe students. The distance teachers seem to communicate more frequently with thestudent group on a daily basis (every day or 2-3 times a week), but more frequently withthe individual student on a long term basis (weekly or more rarely).
e-Learning09Five distance teachers gave their opinions about distance education and two ofthem had very practical comments, one wanted to meet the distance students four timesa semester and the other did not understand how on-line exams, with multiplequestions, could be used in math or bookkeeping subjects. One did not accept that therole of the teachers was to support communication between distance students. Twogave very positive comments of their experience as distance teachers and althoughthey claimed it was a lot of work they were very content or as one said: I am pleasedwith how the distance teaching is coming along and the students seem to be happy inthe distance education. The success is based on a lot of work, I have made a lot of slideshows and recordings and I have weekly assignments. I record the solutions of theassignments and send the students. It is also exemplary how helpful and supporting theadministrators of the distance education are.CONCLUSIONS AND FUTURE WORKThe distance teachers that participated in the survey were almost all over 40years old and claimed they had good preparation for using computers in distanceteaching, although half of them wanted more training in distance teaching methods.They think distance education is a realistic alternative in Icelandic education and aresatisfied with their online courses.The RU guidance for the distance teachers is to use the LMS, record materialand be active sending material to the students and answer their questions and requestswithin 24 hours (except for weekends and holidays). They are also encouraged to useall the options that the LMS offers, e.g. the assignment system, interactive assignments,online discussion and chat. The participants in the study followed the school guidanceup to a limit; they accepted that distance teachers must answer students quickly andwithout fail, although they were not sure about the 24 hours time limit. They were allactive using main features in the LMS and believed it did not control too much of thedistance teaching. They recorded material to send the students and used theassignment system to get students solutions. Other options were used by only half ofthe group or fewer, e.g. online discussion, chat and interactive assignments. Theyagreed that it is important to meet distance students face-to-face and theycommunicated frequently with the student group on a daily basis, but more frequentlywith the individual student on a long term basis. They believed they reached theireducational goals, but 24% were insecure about their ability to teach online.Mumitaz [11] comes to the conclusion, built on a literature review, that it is theinstitution, the teacher and the resources that are the three main factors affecting ICTuse in education. Reykjavik University has offered distance education since 1998, with abroad use of ICT and support for the teachers. The University emphasises goodresources, e.g. LMS and online library, and monitors the development of new options toimplement into the LMS and teachers work. In the survey the focus was on the distanceteachers and the results indicate that they can master the main options of the LMS andwant to be active in their teaching, but what is stopping them for using more onlineoptions? They do hardly need more technical skills as they can manage the LMS andthe recording of the material, but what do they need? Built on his research Barak [12]claims that although teachers use sophisticated technologies, when they are selfstudying, they are careful and sensible regarding the extensive use of ICT in school.(p. 134). This indicates that it is not enough for teachers to know how to use ICT inorder to implement it into their own work; they need more training in how to use it forteaching. This is consistent with the distance teachers answers in this study; they wantmore training in distance teaching methods.The main conclusion is that we need to train the distance teachers in using ICT inteaching and online pedagogic; they have the technological skills needed. The trainingcourses already offered to them seem not enough support for them to implement more- 243 -
e-Learning09opportunities into their distance teaching. Distance teachers need a good knowledge ofnew online opportunities to be able to decide what can be put into practice in thedifferent courses. It is not enough to know what can be done; you need to know how touse it in your online environment. It is a lot of work to be a good and active distanceteacher; you do not only have to preparer material and be active communicating withthe distance students, you must also be up to date in all the new software that is on themarket and be open minded for new opportunities. The school and the distanceteachers must work hand in hand to make sure that we are offering students goodquality distance education.REFERENCES[1] Davis, D. (1989). Perceived usefulness, perceived ease of use, and useracceptance of information technology. MIS Quarterly, Vol. 13, No. 3, 319-40.[2] Kellenberger, D., and S. Hendricks. (2003). Predicting teachers computer usefor own needs, teaching, and student learning. Paper presented at Hawaii InternationalConference on Education, Honolulu.[3] Sadik A. (2006). Factors Influencing Teachers Attitudes Toward PersonalUse and School Use of Computers: New Evidence From a Developing Nation.Evaluation Review, Vol. 30 No. 1, 86-113.[4] Li Q. (2007). Student and Teacher Views about Technology: A Tale of TwoCities? Journal of Research on Technology in Education, Vol. 39, No. 4, 377397.[5] Menchaca, M. P. and Bekele, T. A. (2008). Learner and instructor identifiedsuccess factors in distance education. Distance Education, Vol. 29, No. 3, 231-252.[6] srn Matthasdttir (2008). Distance Education in Iceland. A glimpse into thefuture. Lifelong Learning in Europe, Vol. 4.[7] Statistic Iceland, 2008, hagstofan.is[8] Anna lafsdttir and srn Matthasdttir (2004). Upplsinga- ogsamskiptatkni hsklanmi og kennslu. Tmarit um menntarannsknir, Vol 1,195208.[9] Auur Kristinsdttir, srn Matthasdttir, M. Allyson Macdonald (2001).ttekt fjarkennslu vi Kennarahskla slands. Rannsknarstofnun Kennarahsklaslands.[10] srn Matthasdttir, Michael Dal og Samuel C. Lefever (2003). How dostudents use information and communication technology in Icelandic high schools2002? Proceedings of the 4th annual Conference of the LTSN, Center for Informationand Computer Sciences.[11] Mumtaz, S. (2000). Factors Affecting Teachers Use of Information andCommunications Technology: a review of the literature. Journal of InformationTechnology for Teacher Education, Vol. 9, No. 3, 320 341.[12] Barak M. (2006). Instructional principles for fostering learning with ICT:teachers perspectives as learners and instructors. Educ Inf Technol (2006) 11: 121135ABOUT THE AUTHORsrn Matthasdttir, Assistant Professor, School of Computer Science andSchool of Health and Education, Reykjavik University, Iceland, Phone: +354 5996200,-mail: email@example.com.
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INTRODUCTIONThe analysis of the common trends and features of contemporary educationalpractices identified through research on the system of Bulgarian higher education [1-5]led to building a model to meet the needs of the language learning in the cited context.The VEHICCLE model ((for Vocational purposes) Electronically enHanced InterCulturalCommunicative Language Education) [6] was applied to a course design for the Englishfor Specific Purposes (ESP) blended learning course at Faculty of Mathematics andInformatics, Sofia University. The aim was to develop students of Informaticscommunicative skills in order to facilitate their study and professional realization. Thesettings included using a virtual learning environment (Moodle) and the course website,with face-to-face sessions in a computer lab or traditional seminar room.This paper presents the results of a summative course evaluation to measurestudent attitudes and motivation for the 2008/2009 academic year. Student motivation ismeasured through analysing the perceived effectiveness of the course elements(according to the course objectives) with regards to the motivational aspects (freechoice, anxiety, etc. [7]).The tool used to collect data was a web-based questionnaire, based on the freeversion of Survey Monkey4. It consisted of: bio-data questions (sex, age, group,perceived level of English, attendance rate); closed graded questions investigatingstudent attitude towards the course design and measuring student motivation (based onIntrinsic Motivation Inventory (IMI) [8], adapted for the purposes of this study), whichused Likerts scale [9]; open-ended questions; and an open question for freesuggestions. The open-ended and open questions are not an issue of this paper due tothe limited volume.STUDENT ATTITUDES AND MOTIVATIONThe questionnaire was filled in by 42 of 76 students who completed the course.Bearing in mind that it was presented after the course end, the number of voluntaryrespondents speaks of a clear desire of the students to express opinion and help toimprove the design. Current analysis is based on 41 (n=41) or 39 (n=39) responses (thenumbers of complete answers for the different questions). 30 of the students were maleand 11 female; age (average) 19.75 years. 59% claimed to have attended more than 7classes, which makes the majority attended most of the classes.The first of the graded questions collected data investigating student evaluation ofthe course environment (Moodle and the course website) (Table 1). The scale is: 1never; 2-rarely; 3-usually; 4-often; 5-always. It is evident, that students used the site andthe environment mainly to get informed about the requirements, to get informed about4
Available at http://www.surveymonkey.com/
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e-Learning09the classes, and to use the links and other information provided. The other fourfunctions of the course environment have median at 1 or 2, which speaks that most ofthe answers rated these functions as rarely used. Some of the students admit to haveseen the others work (mean case value at 2.3 and median at 2). Asking questions anddiscussing in the forums score even lower at average (both at mean value below 2). Animpressive half of the students have never used these options. The other half dividedinto usual (20% each) and rare (21% and 17% respectively) use of these types ofcommunication. 78% of the students never used the environments for non-related to theclasses activities.Do you use the VLE(Moodle) and/or thecourse website for:(please rate from 1 to 5)
MEANcase 141MEDIANcase 1-
75th %case
Score 1(%)
Score 2(%)
Score 3(%)
Score 4(%)
Score 5(%)
Table 1. Opinion of the Informatics students in FMI on the VLE for the ESP course
RequirementsclassesInformation (links)To See others workTo askTo discussNot related to classes
It can be concluded that both the VLE and the website were not used up to theirlimits. One reason for this can be the lack of experience for using such environments forstudy, as the students are not acquainted with web-based learning (being in the firstyear of study). Another reason could be the lack of adequate information andinstructions about the environments themselves, and the services and activities theysupport.The second question used the same scale to rate whether the students considerthese classes (without a course book, with computers, in a VLE) compared to thetraditional classes as: more interesting/ more useful/ easier to cope with. Table 2shows, that all three aspects were rated above the neutral point, which is a stable andpositive attitude. The table also shows that the interest scores the highest, next is theease, then comes the usefulness. The two additional answers to the open part of thequestion prove, that the students find this course setting as more fun, improving theattendance and one can decide on oneself when and how to learn. These answersadd to the free choice and enjoyment, discussed later.In comparison to thetraditional classes these are:(please rate from 1 to 5)
MEANcase1- 41
Score1 (%)
Score2 (%)
Score3 (%)
Score4 (%)
Score5 (%)
More interestingMore usefulEasier to cope with
Next question deals with the indicative content: table 3 shows student opinion onthe extent to which the course helped them improve their knowledge on six items. Thescale used is: 1- hasnt helped; 2-helped a little; 3-helped; 4-helped much; 5-helpedvery much. The general opinion is that the course helped the most to know aboutacademic writing: mean value at 3.6, and median at 4. Next come the terminology and- 246 -
e-Learning09the presentation skills, both of which have a median at 3 (helped) and mean case atabout the same value; also about 1/5 of the students have rated them at 5. 30%declared that the course hasnt helped them develop searching for information skills,which brings the mean case at 2 for this aspect. It is evident from the mean values ofthe general vocabulary, that the respondents feel it improved a little by the course(mean at 2.3, and 44% responses at 2). The grammar aspect has the lowest scoreswith 44% at 1; mean at 1.8, and median at 1.5. It is seen that the focus of the indicativecontent (academic writing and presenting) has been acknowledged, studyingterminology is stable at the neutral point (45% at 3), while the other aspects areperceived as complementary rather than main.
MEDIAN case1-41
MEANcase 141
Table 3. Opinion of the Informatics students in FMI on the course indicative content
MEAN case1- 412.953.34184.108.40.206.220.127.116.11.32
MEDIANcase 1- 41333333332232
75% case444333333333
The question about skill development (table 4) was used to provide additionalinformation on the perceived achievement of the course goals (12 aspects), based onthe same scale as above. Similar to the previous question, the highest mean value wasreached by academic writing (3.34), summarizing (3), and information retrieval andevaluation (3). The lowest mean values were for comment writing (2), and readingcomprehension (median at 2, mean 2.3). The other aspects (listening comprehension,speaking - discussion, speaking dialogue, speaking monologue, team working,working in a VLE, searching for information) have a median at 3, which proves that thecourse has provided relatively good opportunities for achieving all the academic skillsdesired. Presenting (monologue and dialogue), and working in a VLE have mean valuesabove 2.5: 2.6, 2.7 and 2.8 respectively, which supports the priorities set before thecourse.- 247 -
e-Learning09The next question investigated student motivation (table 5); it contained 6categories, providing data on enjoyment/ interest (sub-questions I1-I6), perceivedcompetence (C1-C6), effort/ importance (E1-E8), pressure/ tension (P1-P5), perceivedchoice (Ch1-Ch5), value/ usefulness (V1-V6). The scale used is a 7-grade one (1-nottrue at all; 7-very true). The sub-questions appeared randomly, and these marked withan asterisk (*) were worded as a negation (the scores they receive are therefore takenas a reverse value). 39 complete answers were elicited (n=39).
3.9 4.34455
Competence 1: my presentationwas better than average
The interest expressed through the answers to questions I1-I6 complies with thefindings of the previous questions: students find it very interesting to write apresentation (I3), and the course as a whole (I6), both scoring above the neutral point(4) as a mean value and at 5 as a median. Similar is the score of the interest evoked bythe others presentations (mean case at 4.6, median 4, 75% case at 6). The making ofthe presentation (I1), the process of writing (I4), and the discussions (I5) are stable atabout the neutral point, with discussions scoring the lowest (mean case at 3.6). Thesame table contains the perceived competence values: it is seen that the overallsatisfaction with personal performance is stable at the level of 5 for the median andmean cases, and at the extreme 7 for the 75% case. All other sub-questions of thiscategory are slightly above the average point, with own presentation felt at a higher thanthe average level for most of the students (4.6 mean case), which again shows a goodlevel of perceived competence.Table 6 presents the effort/ importance sub-questions, and the pressure felt. Whatcan be observed is that preparing the assignment and presenting it (E1) were notdifficult, though scoring closer to the neutral point (mean case at 3.5) speaks that theywere also not too easy. Lower is only the score of the desire to participate in the forums(E7), with mean and median cases at 3, which supports the conclusions from theprevious questions that the environment has not been adequately used forcommunication. The difficulty of writing academic text (E6) was at the neutral point witha mean at 4.2 and median at 4, which makes it achievable. The ambition to perform wellat the assignment is evident through the mean and median cases of 5 for the desire todo well (E3) and the effort put in it (E4); both have a 75% case at 7. However, theenergy (E5) scores 4.6 as mean case, which is close to the effort for the presentation(E2) (4.3), as both have a median at 5 it is obvious that the declared ambition washigher than the effort. The pressure (P1-5) results confirm this last observation as all- 248 -
e-Learning09score below the neutral point for the mean case, and only the anxiety during thepresentation was considered at 4 as a median case.
5.2 3.45365
3.2 2.33244
4.6 4.2 3543664
The perceived choice as next motivational aspect (table 7) is stable at 4 (for subquestions Ch1 and Ch3) for both mean and median case values. However, the answersof Ch2 and Ch4 prove, that students feel that writing the assignment was not theirchoice; however, it is clear (Ch5) that their perception of choice during the process ofwriting was strong (mean value at 5.3 and median at 6). The results of the questionwhether the students would make an assignment again score at the neutral point, whichmeans that however confident they felt about the choices they have, they are notconvinced that this is something they will need in their future. Yet, they treasure the skillof writing academic text a lot (V1), mean case at 5.3 and median at 6. Similar are thescores about the information about the course (V5), which can explain the overallpositive attitude of the students towards the gaining the course offers. The discussions(V2) and the overall usefulness of the course (V6) score slightly less (mean cases at 4.8and 4.5 respectively, and median at 5), yet well above the neutral point as well.
4.8 45465
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4.3 5.24657
MEAN caseMEDIAN case75th% case
Please rate(from 1 to 7)to what extent thestatements aretrue for youbearing in mindthe ESP course(case 1-39)
e-Learning09CONCLUSIONS AND FUTURE WORKThe results of this study show that students feel all elements of the course (basedon VEHICCLE model) satisfactory and interesting, motivating, and not too difficult todeal with. Their satisfaction with their performance was high, and the feeling that theyachieved the goals of the course is stable, with these goals perceived as a gain for theirdevelopment. The feeling that the course is good, interesting and useful, however, is inslight disagreement with one of the course objectives: to initiate and facilitatecommunication through the environments. A conclusion could be made that moredetailed instructions as to how the environments function is needed so to ensure highergaining from the web-based facilities of the course VLE and site.Additional work is to be done on the answers receive through the open questionand the open-ended ones, which may provide a chance for better understanding thestudents opinion and gather suggestions for further improvements in the course design.As it was mentioned earlier, this paper discussed the evaluation of the coursebased on the answers of students who completed the course. In order to provide moredetailed information and proof of the motivational value of the course further analysis ofthe comparison between the answers of the different categories of students (these whocompleted the course and these who didnt) is needed. To this end additional researchshould be conducted.REFERENCES[1] Kossekova, G. Innovative educational technologies in medical education andresearch methodology. Book of abstracts, International Conference Pioneers ofBulgarian Mathematics, Sofia, July 8-10, 2006, p.66.[2] Sendova, E., E. Stefanova, N.van Diepen. Methodology handbook on ICTenhanced Skills. Faleza, Sofia, 2007.[3] Stefanova, E., E. Sendova, I. Nikolova. I*Teach Methodology for buildingenhanced ITC skills. In: 3rd Balkan Conference in Informatics, Sofia, 2007.[4] Sendova, E., Nikolova, I. (editorial) Innovative Didactics for Web-basedLearning (in Bulgarian). CIST at Sofia University, Sofia ISBN: 978-954-92146-4-2, 2008.[5] Nikolova, I. WWW-based Learning Design and Management: Methodology andTools (in Bulgarian). PhD Dissertation, Sofia University, Faculty of Mathematics andInformatics, 2000.[6] Kremenska, A. VEHICCLE: A Model of Web-based Language Learning. InCompSysTech08, vol. IV-12, ACMBUL, Sofia, 2008.[7] Ryan, R., Deci, E. Self-Determination Theory, Scale Description. 2000,Retrieved 12th April, 2006, from http://www.psych.rochester.edu/SDT/measures/intrins.html[8] Deci, E., Ryan, R. Intrinsic Motivation and Self-determination in HumanBehaviour. New York: Plenum, 1985.[9] Underwood, M. The Likert Scale. 2003. Retrieved 16th June, 2005, fromhttp://www.cultsock.ndirect.co.uk/MUHome/cshtml/index.htmlABOUT THE AUTHORAnelly Kremenska, MSc, MA, Faculty of Mathematics and Informatics, Faculty ofEducation, Faculty of Classical and Modern Philology, University of Sofia,Phone: +359 887 604 681, +359 2 8161 599. -mail: email@example.com.
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INTRODUCTIONIn education, computers can be used to deliver the course content and to measurethe achievement in courses. This thesis dwells upon the assessment techniques ineducation via computers. The British Standards Institution estimates that approximately1000 computer-assisted assessments are performed each day in the United Kingdom[1]. Computer delivered assessments focuses on two specific delivery methods:computer-based test (CBT) and computer-adaptive test (CAT). It have been reportednumerous benefits of the CBT approach over the standard paper-and-pencil one [2-4].These benefits ranged from the automation of marking and subsequent reduction inmarking workload to the opportunity to provide students with immediate feedback ontheir performance [5]. Due to the limitations of traditional success measuring rates, thecontribution for the understanding of students knowledge about a subject is little. Theuse of CBT is an attempt to overcome these limitations and go some way to makecourse and assessments more interesting and useful processes for both teachers andstudents. Notwithstanding these benefits, previous works by researchers suggested thatCBTs have being viewed as unsatisfactory in terms of efficiency. The reason for thisinefficiency is that the questions administered during a given CBT session are nottailored for the specific ability of an individual student. In a typical CBT, the samepredefined set of questions is presented to all students participating in the assessmentsession, regardless of their ability [5]. The questions within this fixed set are typicallyselected in such a way that a broad range of ability levels, from low to advanced, iscatered for [4]. In this scenario, it is accepted that high-performance students arepresented with one or more questions that are below their level of ability. Similarly, lowperformance students are presented with questions that are above their level of ability[4]. The underlying idea of a CAT is to offer each student a set of questions that isappropriate to their level of ability [4]. Generally, a CAT initiates with a random questionwith an average difficulty. A more difficult question follows if the student answers thequestion correctly. Conversely, if the response provided is incorrect, an easier questionthat is suitable for this new lower estimate is presented next [6-7]. The use of CAT hasbeen increasing and indeed replacing traditional CBTs in some areas of education andtraining [5]. Usually this replacement is associated with the need for higher efficiencywhen assessing large numbers, for example, in online training. The study presentedhere focuses on the evaluation of determining item difficulties for CAT software.DATA GATHERING AND PREPROCESSINGThe dataset introduced here consists of a midterm and a final exam taken by 368students during the first semester of History of Civilization course. In the midterm exam,there were 13 multiple choice questions with 4 choices and there were 30 multiple- 251 -
e-Learning09choice questions with 4 choices in the final exam. Questions were delivered via a CBTsystem to the students using separate computers. CBT system was a web applicationrunning on Intranet backbone. Questions delivered during the midterm and final examswere taken into consideration for classification purpose. The reason for classifying thesequestions is to convert the CBT system to a Computer Adaptive Testing (CAT) system.In CAT, items are drawn from the item pool by a simple item selection algorithm so foreach individual examinee, appropriate items are delivered. All items in the CAT itempool range in difficulty values. If an examinee gets an item right, an item having agreater difficulty is selected from the item pool and delivered to the examinee as thenext question of the test. If he/she gets it wrong, then an item having a smaller difficultyis selected from the item pool and delivered to the examinee as the next question of thetest.Table:1 Nominal question levelsQuestion LevelVery EasyEasyMiddleHardVery Hard
Numerical Representation-1-0,500,51
Nominal QuestionLevels00-10-0,5-0,50,50,5-0,5000,51
As seen from Table 2 and Table 4, p-value is indirectly proportional to the itemdifficulty. For instance, the 13th question of the midterm exam is answered by 137 testtakers among 368 test takers. The p-value of this question is 0,372, which shows thatthe probability of getting this question is about 37%. With this probability 13th questionis the most difficult among the midterm exam questions. For the items in Table2, themean and standard deviation is calculated using p values as shown in Table3.According to these mean and standard deviation values, a scale, shown in Figure 1 is- 252 -
e-Learning09designed for placing items into nominal question levels. And all items are placed intorelated nominal question level as shown in below table.Table 3. Mean and Standard Deviation of Item Difficulties found by p-value MethodMean Standard Deviation
Fig.1. Midterm Exam Nominal Question Level vs. Item Difficulty ScaleSame procedure is also applied to the final exam items and nominal distributionsfound after the completing the procedure.Table 4. c, n, p and Nominal Question Levels for the Final Exam using p-value MethodFINAL
Item ID123456789101112131415
Item ID161718192021222324252627282930
e-Learning09content and level of learning. Although most teachers try to select items that sample therange of content covered in class, they often fail to consider the level of discriminationand level of difficulty of the items they use. Item discrimination and item difficulty can becalculated by evaluating the test takers as in norm-referenced item analysis [8]. Itemdifficulty is a measure of overall difficulty (p) of the test item. The lower the p, the moredifficult a particular item is. Whereas, item discrimination tells us how good a question isfor separating high and low performers. It is more important for an item to bediscriminable than it is to be difficult. For norm-referenced item analysis, test takersshould be sorted in descending order first. Then two things must be specified: numberof people in high and low groups and number of people in high and low groups who geta particular answer right. Using these two groups, item discrimination index and itemdifficulty index can be calculated using the below formulas:Item Discrimination Index: a = (Up / U) (Lp / L)Item Difficulty Index:p = (Up + Lp) / (U + L)Where, Up = Number of high performers who got question rightLp = Number of low performers who got question rightU = Number of high performersL = Number of Low performersTable 5. Lp, Up, a and p and Nominal Question LevelsMIDTERM
Item ID Lp Upap140 91 0,42 0,539239 86 0,38 0,514346 108 0,51 0,634435 83 0,39 0,486551 74 0,19 0,514650 100 0,41 0,617728 94 0,54 0,502829 71 0,34 0,412941 98 0,46 0,5721023 93 0,57 0,4771130 99 0,56 0,5311222 98 0,62 0,4941328 65 0,30 0,383
In Table 5 and Table 6, the a and p values for 13 midterm and 30 final questionscalculated by the formulas respectively. Nominal question levels are found using thesame scaling technique in p-value section.Table 6. Mean and Std. Dev. of Item Difficulties found by Norm-Referenced ItemAnalysis MethodMean Standard DeviationMIDTERM
0,513 0,070
0,507 0,116
The higher value of a (up to 1), the better the item is capable of separating highand low performance. If a = 1, this means the entire high performance group and nonein the lower performance group get a particular question right. Since this is not afrequently seen situation, a rarely (if ever) = 1. An item has an acceptable level ofdiscrimination if a >= 0,30 p and a are not independent probabilities. Discriminationindexes less than 0,30 are sometimes acceptable if we have a very high p value. FromTable 5 and Table 7, it is observed that 100% of midterm questions and 93,33% of final- 254 -
e-Learning09questions have acceptable level of discrimination. However, none of them are close tothe optimum difficulty level.Table 7. Lp, Up, a and p and Nominal Question Levels for the Final Exam usingFINALItem
0,40 0,515
0,18 0,429
0,37 0,411
0,40 0,338
0,26 0,498
0,08 0,346
0,26 0,442
0,50 0,485
0,21 0,429
0,39 0,433
0,45 0,532
0,30 0,550
0,12 0,286
0,28 0,671
0,22 0,571
Considering the optimum difficulty index for multiple choice questions with 4options from Table 8, both tests can be termed as easy. But yet, taking student successinto consideration yields presented nominal question levels.Table 8. Recommended Difficulty Index for Various Test ItemsNumber of Options (k)2 (True-False)4Open-Ended
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e-Learning09Table 9. Classification ResultsCorrectlyClassifiedInstances
TotalNumber ofInstances
Percentage ofCorrectlyClassifiedInstances
Percentageof IncorrectlyClassifiedInstances
p value
60.77 %
39.23 %
Norm ReferencedItem Analysis
66.03 %
33.97 %
For this purpose, final exam data will be used since it has more samples. A Rapidlyexploring Random Tree (RRT) is a data structure and algorithm designed for efficientlysearching non-convex, high-dimensional search spaces. Simply put, the tree isconstructed in such a way that any sample in the space is added by connecting it to theclosest sample already in the tree [9]. According to RRT, classification results for pvalue, norm-referenced item analysis are shown in Table 9. As seen from the results thequestions are not classified perfectly, to correct this problem data is made nominal andRRT is applied to the data again. Using the nominal data, the results are fairer.According to RRT, classification results for p-value, norm-referenced item analysis onnominal data are shown below in Table 10.Table 10. Classification ResultsCorrectlyClassifiedInstances
Percentageof CorrectlyClassifiedInstances
90.22 %
9.78 %
95.99 %
4.01 %
e-Learning09that targets in on a respondents true score. So, why are the methodologies of itemresponse theory slow to be adopted into the health care measurement field? Itemresponse theory was developed within the framework of educational testing and somost of the literature and terminology is oriented towards that discipline [9]. A limitationof the modern measurement theory is the complexities of the mathematical IRT models.Most researchers have been trained in classical test theory and are comfortable withreporting statistics such as summed scale scores, proportions correct, and Cronbachsalpha. Beyond the mathematical formulas, there are the complexities of the numerousIRT models themselves as to what circumstances are appropriate to use IRT and whichmodel to use. There is not even a consensus among educators as to the definition ofmeasurement and which IRT models fit that definition. Adding to the burden ofconfusion, the numerous available IRT software in the market are not user-friendly andoften yield different results (parameter and trait estimates) because of the differentestimation processes used by the software. Despite these limitations, the practicalapplications of IRT cannot be ignored. Knowledge of IRT is spreading as more andmore classes are being taught within the university disciplines of psychology, education,and public health, and at seminars and conferences throughout the world. Along withthis, more books and tutorials are being written on the subject as well as more userfriendly software is being developed. Research applying IRT models are appearingmore frequently in health care journals, and much of their concluding comments aredirected towards discussing the benefits and limitations of using the methodology in thisfield. Together, a better understanding of the models and applications of IRT willemerge and IRT will be as commonly used as the methodology of classical test theory.This effort will result in instruments that are shorter, reliable, and targeted towards thepopulation of interest. One further note is that item response theory is only one steptowards the goal of the creation of reliable and valid education measures. Hambletonstates quite well that IRT is not the solution to all of our instrument and measurementproblems [10]. It is a mathematical model only, and when it can be demonstrated that(1) the model fits the data of interest, (2) model parameters are properly estimated, and(3) the model is used correctly, the model has many useful features. But, none of theIRT models [paraphrased] are magic wands to wave over vague instrumentspecifications and poorly constructed items to make reliable and valid measurements.Hard and thoughtful work is still required in defining constructs and related domains ofcontent, drafting items to measure the constructs, field testing, test norming, andconducting reliability and validity studiesIf these steps are not handled well, badmeasurements will follow. However in the sample of this study, best method todetermine the item difficulties is obtained as Norm-Referenced Item Analysis as aconsequence of taking both item discrimination and item difficulty into consideration.Another important thing is to make data nominal before trying to run any classificationmethod on it. The further work to done on this research starts with questioning the sizeof item pool. Since the best method to find item difficulties is determined, both midtermand final exam data is going to be classified since they all differ in number of items.These items will be classified and the importance of the size of item pool will be tried tobe determined by comparing the classification of items in different pools.REFERENCES[1] British Standards Institution (2001). New exam guidelines to stop the cybercheats [online]. Available: www.bsiglobal.com/Corporate/News+Room/exams.xalter[Accessed August 8, 2003].[2] Mason, B. J., Patry, M., Bernstein, D. J. (2001). An examination of theequivalence between non adaptive computer based and traditional testing. Journal ofEducational Computing Research, 24(1), 2939.- 257 -
e-Learning09[3] De Angelis, S. (2000). Equivalency of computer-based and paper-and-penciltesting. Journal of Allied Health, 29(3), 161164.[4] Pritchett, N. (1999). Effective question design. In S. Brown, P. Race, & J. Bull(Eds.), Computer-assisted assessment in higher education. London: Kogan Page.[5] Lilley, M., Barker, T., Britton, C., (2004). The development and evaluation of asoftware prototype for computer-adaptive testing. Computers & Education, 43, 109 123.[6] Lilley, M., Barker, T. (2002). The development and evaluation of a computeradaptive testing application for English language. In Proceedings of the 6th computerassisted assessment conference. Loughborough University, United Kingdom.[7] Lilley, M., Barker, T. (2003). An evaluation of a computer-adaptive test in a UKUniversity context. In Proceedings of the 7th computer-assisted assessmentconference. Loughborough University, United Kingdom.[8] Brown, J. D. (1995). Developing norm-referenced language tests for programlevel decision making. In J. D. Brown & S.O. Yamashita (Eds.). Language Testing inJapan (pp. 40-47). Tokyo: Japan Association for Language Teaching.[9] Hambleton, R. K., Swaminathan, H. (1985). Item response theory: Principlesand applications. Boston: Kluwer-Nijhoff.[10] Hambleton, R. K. (2000). Emergence of item response modelling ininstrument development and data analysis. Medical Care, 38 (9 Supplement), 60-65.ABOUT THE AUTHORSDr. Dilek Karahoca, Department of Software Engineering, University ofBahcesehir, Phone: +90212 381 0567, -mail: firstname.lastname@example.com. Adem Karahoca, PhD, Department of Software Engineering,UniversityofBahcesehir,Phone:+902123810560,-mail:email@example.com Erdodu, Vocational School, Bahcesehir UniversityAssoc.Prof.Dr. Hseyin Uzunboylu, Department of Computer Education andInstructional Technologies, Near East University, Lefkosa, Northern Cyprus.Prof. Ali Gngr, PhD, Department of Computer Engineering, University ofBahcesehir, Phone: +90212 381 0555, -mail: email@example.com.
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INTRODUCTIONThe VEHICCLE model ((for Vocational purposes) Electronically enHancedInterCultural Communicative Language Education) [1], is based on the recognition ofthe changes in both theory and practice of education towards more personal approachto the learners, and setting their skills for lifelong learning and team work for buildingknowledge. What was considered was a number of papers, which generally support theconstructivist pedagogy and describe pedagogical issues, models of e-learning, andcourse design [2- 7] in different contexts.This paper focus is on a course design based on the VEHICCLE model which wassuggested as a solution for enhanced efficiency of using the existing resources (human,equipment, time) in order to meet the demands for achieving higher quality at a lowercost. The first part of the paper goes in depth to describe the two levels [9] of thedesign: the macro level (the objectives, learning outcomes, forms of learning andteaching, and assessment) and the micro level (the content of the model elements whenapplied to the particular course design). The second part of the paper presents theresults of a summative course evaluation to measure student attitudes towards thedesign. The summary of the findings and an outline of the future work comprise the lastsection.COURSE DESIGN BASED ON VEHICCLEVEHICLE defines the components of web-based language learning, thus allowingfor transforming existing curricula and materials for online use; based on the bestpractices [10-15] and established university standards in Bulgaria [16]. Thecommunicative approach to language learning was applied with regards to thecommunal constructivist paradigm, which is regarded as the one to meet the demandsof the complex situation of teaching languages in Bulgarian higher educational context[17, 18]. Based on these results and student suggestions [17], the course design wasimproved in order to ensure collaboration and exchange of knowledge among thestudents, and to boost their performance.Macro Level of VEHICCLE Course DesignThe course is compulsory; semester 1, 2 (first year of education); SpecialtyInformatics at Faculty of Mathematics and Informatics, Sofia University. Theadministrative groups consist of 20 students on average; mixed-ability level ofproficiency in general English, and no experience in e-learning. The learning process isarranged in 60 hours of face-to-face collaboration in computer labs or traditionalseminar rooms, and unlimited collaboration and online support in the virtual learningenvironment (VLE) Moodle.- 259 -
e-Learning09The objectives were to help students to understand the possibilities of informationand communication technology (ICT) in language learning. The emphasis of the coursewas on providing students with means of improving their level of English through apersonalized approach by combining this study with their computer skills. It focused ondeveloping practical skills in making presentations, summarizing information critically,writing academic papers, introducing the standards for writing academic text in English.It also aimed at raising student awareness on terminology and its use in both spokenand written form. Next dealt with ways in which all these skills and knowledge might beused to enhance professional effectiveness.The learning outcomes were set towards: awareness on the roles and possibilitiesof ICT in language learning; competence in ESP (ICT and mathematical terminology);skills: to use Web-based sources of information to study; to make presentations,summarize information, write academic essays; to use English to enhance professionaleffectiveness.The indicative content consisted of: Use of the World Wide Web as a languageresource, reliability; Principles of using terminology: Glossary of IT and Mathematicsterminology; Summary writing; Academic papers: format, plagiarism, citing (standards);Presentations. The forms of learning and teaching were a flexible combination of faceto-face classes and online collaboration (synchronous and asynchronous) on topics andpapers (individual and team work, project work). During the seminars the studentspracticed their productive skills through discussions, comments, glossary compiling,paper writing and formatting. Discussions on various topics were conducted, based ontopics from a course book, or suggested by the students themselves, within the generalsubject of informatics and mathematics. Glossary compiling was introduced as aconcept and a task on producing a glossary on a freely chosen topic (connected withmathematics and informatics) was carried out. Students were presented with the prosand cons of using the Internet as a source of information, thus developing soft skills [4]for searching for information and assessing its reliability. They were also guided throughthe process of academic writing by web-based instructions; through mini projects: tocollect and summarise original texts of their own interest and effectively collaborate intask-solving and decision-making. Other practical skills were expressing orally (formaland informal mode, dialogue and monologue), acquired through discussions andpresentations.The assessment was arranged in three categories, related to the goals of thecourse: oral communicative skills for vocational purposes (presentation) comprise 40%;another 40% depended on the academic writing; and 20% were for class participation(quality postings in the forums to solve the tasks, critical peer-reviews, etc.). The markfollowed the widely accepted scheme 81(and above)% - Excellent; 71-80% - VeryGood; 61-70% Good; 51-60% - Pass; 50 and less Fail. Thus the assessment complieswith the Bulgarian higher education assessment scheme, and reflects the constructivistidea for criteria-based evaluation. The criteria were established and set in advance, andrelated to the skill. For example, the set for the presentation were based on theEuropean Common Framework for Languages for speaking: monologue speech (clearand concise flow of speech); dialogue: asking and answering questions on the topic;discussion skills; presentational skills (e.g. body language, voice, eye contact). It isevident, that the assessment was a complex procedure, taking into account the effortand results of development communicative skills, requiring cooperative work and sharedresponsibility, as well as depending on the peer opinion and participation in the teamactivities. Therefore, as suggested by the constructivist ideology, what was assessedwas not only the result of the learning, but the process of learning as well. Such asystem proved to be well-received by the students [19] as giving sense of developmentand accomplishment, and preparing them for being assessed in their future classes andjob-related situations.- 260 -
e-Learning09Micro-level of VEHICCLE Course DesignThe course design based of the communal constructivist approach is a flexiblestructure; not fixed in advance [20] but rather constantly adapted to the students needs,background knowledge and skills, and constantly re-designed by the studentsthemselves in order to create knowledge with and for the others [21]. Thus the modeldiscussed above can be viewed as scaffolding for the course design, with closelyinterrelated and interdependent elements. Therefore, the description of the micro-levelof the course design follows the elements of the model as identified above [1].The Module is the main structure of the model describing the process of learning. Itcontains a number of elements, which may vary in contents and number from module tomodule, and may describe procedures of different length. For example, the introductorymodule of the course under discussion contained instructions on how to use the coursesite/ the VLE, teaching materials to test the main features of the environment, and someimmediate feedback and support in order to assist all students to get acquainted withMoodle. The instructions were basically of two types: the first type contained a unit,devoted to a topic (based on a course book; on good practice; or student-generated);and the second directions on how-to concerning the academic paper writing. Theflexible list of topics started with classes on Netiquette, Searching Engines, What isinformation?, which proved to be essential for introducing students to onlinecollaboration, in turn crucial to their effective study in a blended-learning mode. Thetypical instruction list contained: information on the specific goals of the current class; abrainstorming part a question to elicit background information on the suggested topic;some collaborative or pair tasks, mainly containing links to materials, available online orfrom the Repository; homework, or self-directed work (related to creative thinking,others work assessment, or additional reading); post-activities to share and discuss theresults of the homework; suggested further reading links to stimulate deeperinvestigation on the topic. Normally, the brainstorming was done in a face-to-facecommunication; the tasks were a mixture of face-to-face communication andcollaboration online, ending with a posting in the respective Forum; and the homeworkand post-activities were a topic for face-to-face or online discussion in dependence onthe importance of the topic or the student interest in it.As a study showed [19], fixed content of the course in the field of informationtechnology terminology were regarded as outdated, thus not interesting for the students.Therefore, the teaching/learning materials were an open collection of previous yearsassignments; topic-related lists of links to websites, articles, videos, etc.; and materials,developed or suggested by the students themselves. They could be divided into corematerials (such as instructions for the class, with links to basic information on a giventopic to be read/ listened to); and additional materials (further reading, presented as alist of links or as tasks for searching for external sources). Examples of teacherdeveloped core materials are PowerPoint presentations (e.g. Writing Academic Paper,Online Dictionaries); handouts with the instructions and links; a website for the coursecontaining the Repository with all handouts, a calendar, forums, contacts, etc. Differentmodalities (types of multimedia and available technical solutions) were explored in orderto facilitate learning for students with different learning styles and abilities. Theaccessibility of the materials was ensured by being uploaded to the online Repository(adapted for blind students) and linked to the web-based course. All the materials in theRepository were described in a LOM-compatible interactive template [21], speciallydeveloped for the VEHICCLE Model.The self-assessment was based on different tests, supported by Moodle plug-ins(e.g. a Hot Potatoes close test to fill in terminology discussed during the classes),comments on the teamwork on the assignment, as well as peer-reviews of the postingsfor the tasks from the instructions.- 261 -
e-Learning09The feedback and the assessment were commissioned by all students and theteacher/facilitator using all modalities of computer mediated communication. Forexample, peer-reviews were issued for the task postings in the forums; personalmessages and e-mails were sent to the students at different stages of assignmentdevelopment to suggest changes and improvement. The process of giving and receivingfeedback from the peers was rarely monitored, which gave the students the relativefreedom to express themselves in a less threatening atmosphere, negotiating meaningand arguing for and against a statement.The assignment (the academic text and the presentation) is the product of thelearning process, and is the other main part of the model [1]. The preparation waspartially a classroom activity (especially at the stages of selecting information, andplanning the draft), and partially a team or individual work out of class; either wayguided and facilitated by instructions. The topics of the assignments were chosen by thestudents, on the condition that they did not repeat and were in the field of Informatics orMathematics. The importance and relevance of the topic to the course goals had to beproved. Students also decided who their team would consist of, how to divide the workwithin the team, and when and where to work. Clear assessment criteria andrequirements were set in advance.All of the elements were situated in the web-based learning environment (Moodlein this case) or linked to it (the course website). The main functions of the onlineenvironment were: to inform about the course goals, instructions, topic choice; to makematerials and relevant external sources available; to allow for course management,through monitoring and guidance; to provide a calendar for the deadlines andforthcoming course events; to facilitate communication, both synchronous andasynchronous, among the students, and with the lecturer; as well as to ensure feedbackand peer-support.As a whole, the elements of the micro level of the course design were arranged tosupport the macro level. As made clear during the description above, all stages of thelearning process and product development rely heavily on both face-to-face andcomputer-based communication, as illustrated by VEHICLE. Another important featureof this course communication was that it was purposeful: the target language was usedto solve real problems during the team tasks. Thus a student dominated and initiateduse of the target language as a medium was ensured.SUMMATIVE COURSE EVALUATIONIn order to pilot the new design, a series of experimental ESP courses wereintroduced between 2005/2006 and 2008/2009 academic years. The current summativecourse evaluation is part of continuous investigation aiming at course designimprovement [18, 22] and was carried out to collect feedback from the students. Thetool used to collect data from the students was a web-based questionnaire, based onthe free version of Survey Monkey5. The questionnaire consisted of four categoriesquestions: bio-data questions, questions investigating student attitude towards thecourse design, questions measuring student motivation, and an open question. Due tothe limited volume of this paper, this analysis is based on the quantitative questionsconcerning the course design. The scales used for the closed questions are standardLikerts scales (5 and 7-graded; neutral point at 3 and 4 respectively).The questionnaire was filled in by 42 of 76 students who completed the course. 30of the students were male and 11 female; average age 19.75 years. The studentperceived level of English was high: 43% declared language competence at C16, 40%at B2, and 9.5 % (4 students) at C2. The average participation is between 7 to 10
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In comparisonto thetraditionalclasses theseare: (rate from1 to 5)case 1-41
more interesting
more useful
easier to copewith
The coursewas:(rate 1 to 7)case 1- 39
very interesting
adequate to myneeds
very useful
I am satisfiedwith myachievements(rate 1 to 7)
MEAN caseMEDIAN case75% case
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e-Learning09[2] Kossekova, G. Innovative educational technologies in medical education and researchmethodology. Book of abstracts, International Conference Pioneers of Bulgarian Mathematics,Sofia, July 8-10, 2006, p.66.[3] Sendova, E., E. Stefanova, N.van Diepen. Methodology handbook on ICT-enhancedSkills. Faleza, Sofia, 2007.[4] Stefanova, E., E. Sendova, I. Nikolova. I*Teach Methodology for building enhancedITC skills. In: 3rd Balkan Conference in Informatics, Sofia, 2007.[5] Sendova, E., Nikolova, I. (editorial) Innovative Didactics for Web-based Learning (inBulgarian). CIST at Sofia University, Sofia ISBN: 978-954-92146-4-2, 2008.[6] Nikolova, I. WWW-based Learning Design and Management: Methodology and Tools(in Bulgarian). PhD Dissertation, Sofia University, Faculty of Mathematics and Informatics, 2000.[7] Kossekova, G. Web-based Education in Biochemistry (in Bulgarian). Dissertation,Medical University, Sofia, 2004.[8] Beetham, H. Review: developing e-Learning Models for the JISC PractitionerCommunities, 2004. Last accessed on 17th April, 2009 at http://www.jisc.ac.uk/uploaded_documents/Review%20models.doc[9] Beam, P. A learning revolution - It's about time! (Online-learning.com), 2000. Lastaccessed on 13th March, 2006 at http://www.online-learning.com/papers/ articlerev.html[10] Mason, R. Course Design to Enhance Learning (LTSN Generic Centre). 2003. Lastaccessed on 15th May, 2006 at http://olkg.cranfield.ac.uk[11] Center for Instructional Development and Research. University of Washington Seattle.Lastaccessedon14thApril,2006athttp://depts.washington.edu/cidrweb/ConsultationServices.html[12] City University, London. Guidelines For Developing A Web-Based Course CityUniversity. 2005. Last accessed on 15th June, 2006, at http://www.city.ac.uk/mediadevelopment/e-learning/guidelines/print/printversion.htm[13] Scott, B. Course Design. On-line Knowledge Learning Garden. Cranfield University.Last accessed on 29th March, 2006 at http://olkg.cranfield.ac.uk/[14] University of Wisconsin-Milwaukee. Web Course-ware Standards. University ofWisconsin-Milwaukee P.O. Box 413 Milwaukee, WI 53201 (414) 229-1122, 2002. Lastaccessed on 29th March, 2006 at http://www.uwm.edu/[15] Peytcheva-Forsyth, R. University Course Design ( )(in Bulgarian). Sofia University Press, Sofia, 2002.[16] Kremenska, A. ICT in Language Education: Practice and Perspectives (in Bulgarian).A collection of scientific articles Of Human and of Language, pp. 623-639. Sofia UniversityPress, Sofia, 2007.[17] Peytcheva-Forsyth, R. E-Learning: theory, practice, aspects of the design (inBulgarian). Publishing House Daniela Ubenova. Sofia, 2009.[18]Kremenska, A. Measuring Student Attitudes to Computer Assisted LanguageLearning. In CompSysTech07, vol. IV-5, ACMBUL, Sofia, 2007.[19] Shopov, T. Foreign Language Teaching Methodology (in Bulgarian). Sofia UniversityPress, Sofia, 2002.[20] Holmes, B., Tangney, B., FitzGibbon, A., Savage, T., Mehan,S. CommunalConstructivism: Students constructing learning for as well as with others. Tech-reports, TrinityCollege Dublin, 2001. Last accessed on 10thApril, 2009 at https://www.cs.tcd.ie/publications/tech-reports/reports.01/TCD-CS-2001-04.pdf[21] Kremenska, A. E-Learning for Language Education: A Template for EducationalObjects (in Bulgarian). Conference proceedings 120 Years Pedagogy at Sofia University, pp.346-353. Sofia University Press, 2008.[22] Kremenska, A. Investigating Student Readiness for Computer Assisted LanguageLearning. In CompSysTech08, vol. IV-13, ACMBUL, Sofia, 2008.
INTRODUCTIONThe term technology in the education is extended and this finds an impression inthe direction of the system analysis and the teaching process projection with the tools ofthe information and communication technologies. To the modern pedagogical science isset the actual task to educate and prepare the rising generation in order actively toparticipate in the new stage of the society development, related with the automatedprocess.A lot of research work about the effectiveness of e-learning has been made thelast few years. One of the reasons for this is that the implementation and usage of thee-learning systems and applications requires large investments of time and money.The term effectiveness of e-learning is abstract and there are a lot of definitions,which differentiate from each other mostly because they offer various factors for itsdefining and assessment.In this report we accept the following concept - the effectiveness of the e-learningin the modern education can be guaranteed if all the components in this process(teachers, students and software developers) possess the necessary characteristic,knowledge and skills.The five fundamental aspects of e-learning are organizational, technological,curriculum design, instructional design, and course delivery (delivery system). Thedifferent delivery systems have various methods for date collection.By the usage of e-learning technologies, the exchange of the information isrealized in a higher degree. This lets to some requirements for the teachers andstudents and undertakes shared responsibilities in the process of teaching and training.In order the potential of these innovative technologies to be used in the mostappropriate way, some competencies for working with them are necessary.Some of the success factors for e-learning are leadership issues and competent,knowledge and skills of the teachers, necessary for ensuring the effectiveness of elearning. It is crucial to include the staff in the changing processes in education. It isalso important to appreciate the motivation of learners.The goal of the report is to define the necessary basic and specific skills andcompetencies of the teachers in using e-learning technologies and their usage in thedifferent educational levels.BASIC, SPECIFIC AND DIDACTIDAL COMPETENCES, KNOWLEDGE ANDSKILLS OF THE TEACHERS FOR ENSURING THE EFFECTIVENESS OF ELEARNINGTeachers basic competences, knowledge and skillsThe teacher plays a key role in the development and management of the teachingprocess using ICT. The main difference between the electronic form of teaching and the
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e-Learning09traditional one is the transformation of the didactical approach teaching by narrationinto teaching, orientated towards the students.The application of the e-learning technologies can be discussed in two aspects:firstly the educational practices, which are orientated towards applying the interactivemultimedia in order to present more attractively the teaching content and secondly theweb-based forms of didactical materials and tools and their implementation asinteractive multimedia projects. Figure1 shows the existing e-learning technologies andtheir application in the different educational levels.For the right apply of the modern ICT like e-learning in the teaching and learningprograms, didactical criteria for their applying have to be involved: Effectiveness and expedience. An important aspect in applying the elearning technologies in the education is their effectiveness and expedience.The teachers have to be able to access and use the e-learning tools. Pedagogic competence The pedagogic competence is defined from theteaching content, the concrete didactical goals and the age of the trainees.The usage of the ICT have to be implemented from the teacher of theconcrete disciplines, who must know when, where and how to apply them inhis daily work.
High school
Secondary school
Fig.1. The e-learning technologies and their usage in the different education levelsThe necessary competences knowledge and skills, which the teachers needs, inorder to use the e-learning technologies in the different education levels are: Basic knowledge and skills for working with a multimedia computersystem, which are necessary in computer-assisted learning and computerbased learning; Basic knowledge and skills for working with the Web environment, whicharenecessaryinWeb-basedlearning,Web-assistedlearning,Teleconference, Videoconference.Basic knowledge and skills for working with a multimedia computer system are [4]:9 Fundamental knowledge about the advantages of the ICT integration in theteaching process;- 266 -
Knowledge about the methods and tools for data accumulation, preservation andoperation;Knowledge about the computer system organization technical equipment andwork principles;Knowledge about the basic peripheral devices, which are part of the modernmultimedia computer configuration;Knowledge and skills for applying the program tools, which maintain the computersystem;Knowledge about the potentiality of the software applications, with commonpurpose for text processing, creation and processing of graphical images andelectronic tables;Skills for developing presentations with teaching content;Skills for applying the basic ways for communication and data searching in theInternet environmentBasic knowledge and skills for working with the Web environment are:Capabilities for working in a visual environment;Knowledge about the current electronic tools with teaching content and skills forapplying them in their daily work;Knowledge about the program environments for development of Web-basedapplications and skills to prepare such materials;Knowledge about the potentiality of the electronic devices for communication in realtime;Skills for knowledge transfer.
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e-Learning09university, self-teaching, specialized qualification courses for applying the ICT in theteaching process, other ways.Some of the serious disadvantages of this qualification process are the following:9 For the teachers with a long practice teaching it is difficult to adapt to thetechnical system and the new information environment;9 The trainers sometimes are not very good specific qualified (pedagogicaland technological);9 The groups of the trainees are formed without a serious examination fortheir preliminary computer skills and knowledge.The principle for mastering the knowledge in the field of computer andcommunication technologies in the direction of workability of this knowledge in theprofession of the future pedagogues is applied in the continuous actualization of theschool plans and programs in the computer university disciplines [2]. The motivationand the requirements of the students (future pedagogues) are the most important factorfor their mastering in that area. From one side are the disciplines, giving the necessaryknowledge and skills for working in a computer-communication environment and appliedsoftware as an instrument for automation of the information activities and on other sideare the disciplines, related with the usage of specialized program platforms as ateaching tool that will be applied for their future work. This, in the maximum rate,regulates the workability and the effectiveness of the teaching process in the differentspecialties [7].The future pedagogues should be taught to use the different program tools inconcrete activities and aspects. The types of the program tools that may have place inthe school schedule in the school, according to their functional purpose are:9 Pedagogical program tools (applied programs), intended to present the coursematerial and to manage the teaching process;9 Diagnostic, testing programs, that aim at finding the reasons for the wrongbehavior of the students, assessment of their knowledge, skills, determination ofthe level of their preparation and the level of their intellectual development;9 Instrumental program tools for constructing program tools with teachingpurpose, preparation and generation of methodical and organizational materials,creating graphic, sounds, etc.9 Subject-oriented environments for modeling the studied objects and theirrelation in a program environment.9 Program tools for atomization for processing the scientific experiment results.9 Management program tools their aim is to manage real objects ( for examplerobots activities)9 Program game environments for cognitive activities by different types of gameswith computer.The necessity of lifelong qualification makes every person, including the teacher,student till the end of his life.Didactical aspects necessary for ensuring the effectiveness of e-learningBy applying the e-learning in the education is ensured flexibility adapting todifferent situations, needs, etc. and accessibilities opportunity to study in any momentand in any time. It is necessary the content and the tools for teaching andunderstanding to be deeply taken into consideration, in order the most effective way forthe concrete theme and subject to be applied [1], [6].Before the usage of the computer and their potentiality in a proper way, theteachers have to transform the technique in convenience, in order to be appropriateteaching tool. The instructors have to be free to define what is important and how to
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e-Learning09apply it in their work and creatively to combine the classical with the new tools andforms of the education [5].The aim of the disciplines, related with the use of e-learning as a teaching tool isnot only the technical knowledge and skills for working with the program teachingplatforms, but also they have for an object to convince the teachers, to motivate themand to show them the advantages and the necessity from the modern tools and forms ofeducationThe basic aspects in using the ICT in the education are: Directly in the teaching and learning process when new knowledge have to bepresented, also when practice and control of the results from the teaching processhave to be implemented. The usage of ICT directly in the school process makesthem an instrument, by which it is taught and the students can learn. This is moreand more observed as a practice in the class, because the teachers realize thegreat effect and the advantages of this practice For self-learning by realization of the potentiality of the ICT in the learning systems. For distance learning by using the tools of the Internet technologies. For preparation of course materials - by the usage of object-oriented programtools and software platforms (specialized applied software or with commonpurpose) for preparing different course materials and subsidiary documents withthe aim to form school activity culture from the side of the teachers and directed tothe students.Didactical parameters and indicators of the e-learning coursesIn order the necessary knowledge and skills to be successfully overcome and thegoals of the e-learning courses to be reached, a very professionally structure has to bedeveloped. This structure has to be developed according to the following parametersand indicators Figure 2:The coursesyllabus
The coursecontent
Parameters andindicators of thee-learning courseThe studentsachievements
Fig.2. Parameters and indicators for ensuring the effectiveness of the e-learningcourses
The course structure has to be actual, in accordance with the needs andcapabilities of the studentsThe course content has to be defined before the teaching process itself and afteran analysis of the specific features of the students (specialty, qualification, level ofthe knowledge and skills in the field of the computer science and technologies); theapplication of the computer systems and technologies in the concrete professionalenvironment, as well as of the concrete problems, that this study must be able tosolve. The analysis have to distinguish the concepts must know and it will bebetter to have knowledge about This content has to define the concreteknowledge, skills and habits that the students are required to have, after finishingthe course.- 269 -
INTRODUCTIONThe present research starts its developing from the concepts and principles of theinnovative network technologies in the field of Internet programming as well as theircurrent directions.There has been published little work on the use of .NET technologies for LearningSupport Environment (LSE). Thats why the authors see major advantages in usingnetwork technologies as data services.The purpose of the report is to offer a smart framework that interweaves keynetwork technologies of Microsoft with the standards and specifications of LearningManagement System (LMS), as Sharable Content Object Reference Model (SCORM),IMS Content Packaging, IEEE Learning Object Metadata (LOM), and InstructionalDesign Theory.These standards need supplements in order to apply instructional design theoryand personalized access to learning objects, which are achieved by the framework andprogram realization in LSE enclosed in the present report.Applications demonstrated avoid the clumsiness of most of LMS built by verycomplicated software modules thanks to application of the ADO.NET Data Servicesarchitecture and concentrating the whole content in Learning Repository realizedthrough Microsoft SQL Server.DESCRIPTION OF THE ADO.NET DATA SERVICES ARCHITECTUREADO.NET Data Services represents architecture that uses Representational StateTransfer (REST) - style web services and provides architecture for consummating ofresources in Entity Data Model through URI and relevant program code.
e-Learning09The System.Data.Services.Client library provides clients application withpossibility of execution of Language INtegrated Query (LINQ) requests and to preservechanges in Learning Repository (Database) through ADO.NET Data Services.Services use the ATOM and JSON formats for data representation. Services useURIs for location of the data.ADO.NET Data Services provides the architecture to consume resources and mapthem into entities, expose an entire entity model via URIs, and customize the servicesby adding specialized service operations and the means to intercept service calls andperform business rules [4].ASP.NET and Web services have been used in the initial application of the modelfor describing, structuring, and organizing the learning objects.REST services differ from SOAP web services in that they use the HTTP verbs andthe URI to describe the service operations (what a service can do). Therefore, RESTservices have no need for a WSDL to describe service operations. REST services usethe verbs of HTTP to describe the action of the message in terms of Create, Read,Update, and Delete (CRUD) semantics [6].Web services use approaches limiting the application of the new Model fordescribing, structuring, organizing, and personalizing the learning objects. Due to thespecified reason the model is accomplished by applying the ADO.NET Data Services,ASP.NET 3.5 and Silverlight 2 technologies, and C#.Some of the benefits realized from their experience are noted below:- To promote reuse the developer has purposely not included any user interface(UI) components in the data services project. It is best practice to follow this approachand separate the UI and data services into their own projects.This enables the data service to be easily consumed by multiple user interfacessuch as .NET clients, Silverlight, and ASP.NET AJAX clients [6].- The LearningObjects.svc data service that the authors create in these scenarioscan be consumed by various clients.- ADO.NET Data Services takes the best features of RESTful-style web servicesand makes them easier to implement over an entity model, and it provides features toaccount for authorization, business logic, and custom service operations [4].- ADO.NET Data Services supports multiple data formats.FRAMEWORK FOR THE APPLICATION OF NETWORK TECHNOLOGIES ANDSTANDARDS FOR E-LEARNING IN BUILDING OF LEARNING SUPPORTENVIRONMENTFramework includes architecture for distributed systems and particularly forbuilding of learning support system and model for describing, structuring, organizing,and personalizing the learning objects.The architecture includes the following layers:- Clients application built through the Microsoft ASP.NET 3.5 and Silverlight 2technologies- Server application realizing data services through ADO.NET Data Services- Database presented by Microsoft SQL ServerThe software application of the framework has been achieved on MicrosoftWindows Vista SP1, IIS 6.0/7.0, Visual Studio 2008 SP1, C#, SQL Server ExpressEdition, LINQ to ADO.NET Data Services, Microsoft .NET Framework 3.5 SP1,ASP.NET 3.5, Silverlight 2, Internet Explorer, and Fiddler2. ADO.NET Data Services ispart of the ADO.NET 3.5 Service Pack 1 that resides in Visual Studio 2008 SP1.Development of the RESTful service LearningObjects.svc includes the followingstages:1. Development of Entity Data Model (EDM) - LearningRepositoryModel.edmxbased on the LearningObjectsRepository.mdf database.- 272 -
e-Learning09The Entity Data Model - LearningRepositoryModel.edmx, shown in Figure 2,exposessomeofthetablesandentityrelationshipsintheLearningObjectsRepository.mdf database. The association stage of entities is illustratedin the following figure:
e-Learning09Object Metadata Standard [9] offered nine categories: general, lifecycle, metametadata, technical, educational, rights, relation, annotation, and classification.They are suitable for searching the learning objects in the databaseLearningObjectRepository.mdf of the server and the access to the Learning Repositoryis made through ADO.NET Data Service - LearningObjects.svc.The manifest of each content package include the necessary information fordefining adaptation rules which determine how learning objects should be selected fordifferent learner profiles [5].In conformity with the IMS Contents Packaging Specification [8] the contentpackage in the environment includes two components an XML file describing thecourse structure and the physical files forming the course structure.Applying the new network technologies and particularly ADO.NET Data Services,ASP.NET, and Silverlight 2 the authors offer a new solution, in which learning objectsand meta content will be presented in tables in MS SQL Server.Module 3: Personalization moduleThe personalization module constitutes collection of adaptation techniques thatare intended to tailor a course for the individual learner. The intention is to optimizeadaptive selection of alternative course material and characteristics/requirements.Based on the selected learning design, which is essentially a hierarchy of learningactivities, the module is able to bind specific learning objects to each activity usinginformation from the learners profile and it builds an intermediate representation of thelearning experience [1].The learning activity is a meaningful unit of instruction that provides a learningobject to the learner or several sub-activities.The sequence of launched content objects, for a given learner and contentstructure, provides a unique learning experience (learner interaction with contentobjects) [7].The navigation rules define how different learning objects are selected for differentlearners and specify the matching between the learner profiles and learning content [5].The module guides the construction of adaptive learning experiences and allowsfor the binding of appropriate learning resources at run-time according to the learnerprofiles. Representation of the course concepts and their semantic relations separatedfrom the physical content allow for the binding of appropriate learning objects to thelearning scenarios at run-time.Module 4: Instructional design[11]The existing standards and specifications offer rules which do not follow anyspecific strategy of study. Therefore, the authors have used the instructional designtheory.Following this theory the following key processes for study using the Internet canbe specified included in the learning support environment.Modelling the user is enabled to follow the accomplishment of a particular taskstep by step using the offered multimedia application in the environment, whichfacilitates the process of explanation and modelling.Scaffolding and fading these are learning technologies, where the assistancegiven by the environment slowly reduces and the responsibility of the user fordeveloping an independent software application increases.Articulation and reflection the users need an analysis of their actions. One of theadvantages of the developed learning environment is the possibility to record currentactivities and results for a subsequent analysis. After the user has mastered therespective course subjects, he/she is given graded tests for examining the acquiredknowledge.- 275 -
e-Learning09CONCLUSIONS AND FUTURE WORKAnalyzing the most promising network technologies and the existing standardsand specifications for the development of learning environments the report authors haveachieved the framework development and have explained its application for thedevelopment of a single LSE.In order to understand and reduce the complexity of the e-learning process, datastorage, and application logic, the authors of the report propose the focus of theframework to be ADO.NET Data Services representing RESTful data.Applying the new network technologies and particularly ADO.NET Data Services,ASP.NET 3.5 and Silverlight 2, the authors offers a new solution, in which learningobjects and meta contents shall be presented in tables in MS SQL Server.The personalized web-based environment allows execution of publishing, search,exchange and multiple uses of the learning objects. Each learner has access to thematerial that meets his requirements, interests and skills.One of the most important advantages of the developed environment is that theuser interface and data services are realized in independent projects in order to bepossible different user interfaces to be used in case of future changes in theenvironment the adoption of another type of interface to be easier.Proposed realization of the framework may be applied in its entirety or partially inlearning support environments that may be adjusted by the teachers themselves withoutthe interference of development teams.REFERENCES[1] Arapi, P., N. Moumoutzis, M. Mylonakis, G. Stylianakis, G. Theodorakis, S.Christodoulakis. Design, Implementation and Experimental Evaluation of a PedagogyDriven Framework to Support Personalized Learning Experiences.- Cross-Media andPersonalized Learning Applications with Intelligent Content: Second LOGOS OpenWorkshop. Varna, 2008.[2] Dayley, B., L. Dayley. Silverlight 2 Bible. Indianapolis, Indiana: Wiley, 2008.[3] Mayo, J. C# 3.0 Unleashed with the .NET Framework 3.5. USA: SAMS, 2008.[4] Papa, J. Data-Driven Services with Silverlight 2, 1st Edition. USA: O'Reilly,2009.[5] Sampson, D., C. Karagiannidis, F. Cardinali. An architecture for Web-based elearning promoting re-usable adaptive educational e-content. Educational Technology &Society 5(4), 2002.[6] Shaw, J., S. Evans. Pro ADO.NET Data Services: Working with RESTful Data.New York: Apress, 2009.[7] http://www.adlnet.org/[8] IMS Instructional Management System project,http://www.imsproject.org/[9] http://ltsc.ieee.org/wg12/[10] http://msdn.microsoft.com[11] Petrova, V. Main tools for Learning Support Environment development.CompSysTech2005, Varna, 2005.ABOUT THE AUTHORSValentina Petrova, Department of Computer Systems and Technologies, St. Cyriland St. Methodius University of Veliko Turnovo, Bulgaria, E-mail: email@example.com. Prof. Margarita Todorova, PhD, Department of Computer Systems andTechnologies, St. Cyril and St. Methodius University of Veliko Turnovo, Bulgaria,email@example.com- 276 -
1. INTRODUCTIONThe global Information Society is organized by different information resources andactivities and the e-learning (as a part of the European e-governance) is an importantcomponent [1]. In this reason the distributed learning (DL) unites the principles of elearning and networking and M. Bowman write in [2]: Distributed learning is not just anew term to replace the other DL, distance learning Distributed learning is aninstructional model that allows instructor, students, and content to be located indifferent, noncentralized locations so that instruction and learning occurs independent oftime and place. The distributed approach permits to use the contemporary concepts forsemantic Web as ontology in the e-learning scenarios, building the networks foreducational media exchange, personalized access to the distributed informationresources, intelligent systems, etc.The model of DL is connected with the concept of distributed and common-usedinformational resources in a heterogeneous environment with local management. In thisreason the design of such distributed learning environment (DLE) requires to build anadequate conceptual model [3] and previously investigation based on formalization andmodeling of the processes of information servicing. This will increase the effectivenessof the architectural and software design [4, 5] and it has different methods and tools formodeling and investigation of information processes. The information servicing in theDLE could be regarded as a sequence of events that each of them could be realized if aset of conditions is executed. This approach permits to use the discrete apparatus of thePetri Nets (PNs) [5, 6].The paper presents the initial phases in a DLE investigation by PN modelconnected with modeling organization. In this reason a formalization of DLEcomponents and processes of the information servicing is given. This formalization isbased on proposed conceptual model and is used as a basis for model primitivesdesigning. These basic models permit to build a general PN model of DLE for itsdeterministic investigation.2. FORMALIZATION OF INFORMATION SERVICING BY USING PETRI NETSA conceptual model for organization of DLE is proposed in [3] and a generalizedscheme is shown in fig. 1. DLE is planned as a multi-user and interactive environmentfor knowledge presentation in the area of 3D simulation and virtual reality organization.Each 3D simulation will be executed on the server and will be visualised by the clientbrowser. The access to the learning resources could be realized from different remotenodes by communication resources of Internet. The main components defined in thisconceptual model are users, information learning resources and communication- 277 -
e-Learning09medium. All these components could be described as discrete independent units withinternal structure and own functionality. This permits to use the deterministic apparatusof Petri Nets for organization of DLE modeling and previous formalization.Communication mediumClient
means, situated in the separate nodes of the DLE permitting multi-user access.- 278 -
e-Learning099 T = {Tq / q = 1 K } , T (Transmitters) network technical and technologicaltools for information object transmission trough the communication medium of the DLE;Tq9 Distributor ( D ) it distributes and routs all requests req : U i R j andqinformation objects Inf : R j U i and unite the algorithms, protocols and other
R = {R j / j = 1 M }
U = {Ui / i = 1 N}
reqUi U
Rj R
e-Learning09p3 presence of information block in the output buffer.T = {t1 , t2 , t3 , t4 } T = 4P = { p1 , p2 , p3 } P = 3
I (t1 ) = { p1 , p1}I (t 2 ) = { p3 }I (t 3 ) = { p 2 }I (t 4 ) = {In _ U DOUT }Input and output matrixes:p1p2p3DIND2000t1t20010t30100t40000
O(t1 ) = { p1 , p 2 }O(t 2 ) = { p1}O(t 3 ) = {Out _ U DIN }O(t 4 ) = { p3 }D+t1t2t3t4
P = { p , p , p , pD } P = 4I (t R ) = { p A , p B }I (t S ) = { pC , pC }
T = {t R , t S } T = 2O(t R ) = { pC , pC }O(t S ) = { p B , p D }
00- 280 -
T = {tT 1 , tT 2 } T = 2
P = { pT 1 , pT 2 } P = 2I (tT 1 ) = {DIN , pT 1}I (tT 2 ) = {Out _ R, pT 2 }
O(tT 1 ) = {In _ R, pT 2 }O(tT 2 ) = {DOUT , pT 1}
In_R Out_R
e-Learning095. CONCLUSIONThe model for distributed learning is connected with the concept of distributed andshared information resources in a heterogeneous environment with local management.On the side of the information process the d-learning permits to all basic learningparticipants (teachers, students, informational resources) to be placed in differentterritorial distributed places (different nodes of the DLE). This provides multi-useraccess supporting and personalization of resources use.The presented formalization and designed model primitives determine the base forcarrying out an investigation of the learning and information processes in the DLE. Inthis connection the next investigation phase is a generalized Petri net modelconstructing and its execution. In order to obtain a good result is necessary to make aprevious verification of the designed primitives.ACKNOWLEDGMENTThe research presented in this publication is funded by the Internal ResearchProject 091001-09, Research and Development Sector at Technical University ofSofia, 2009.REFERENCES[1] Romansky, R., I. Noninska. e-Learning as a Part of the European eGovernance. Communication & Cognition, Belgium, 1&2 (vol. 41), 2008, pp.83-94.[2] Bowman, M. What is Distributed Learning? News and Advice from theTechnologyCollaborative,1999,vol.II,issueI(http://techcollab.csumb.edu/techsheet2.1/distributed.html).[3] Romansky, R., E. Parvanova. Conceptual Model of Distributed Architecture for3D Simulation Learning). Proceedings of the Intl Scientific Conf. UNITECH08 Gabrovo,Bulgaria, 21-22 Nov. 2008, pp.III-287-III.292 (in Bulgarian).[4] Ji, Y., K. Chang, P. Bobbie. Interactive software architecture design withmodeling and simulation. Proc. of the 42nd Annual Southeast Regional Conference,April 2004, pp. 305-306.[5] Volovoi, V., Kavalieratos, G., Waters, M., Mavris, D. Modeling the reliability ofdistribution systems using Petri nets. Proc. of the 11th International Conference onHarmonics and Quality of Power, 12-15 Sept. 2004, pp. 567-572.[6] Falkman, F., B. Lennartson, M. Tittus. Modeling and specification of discreteevent systems using combined process algebra and Petri nets. Proceedings of theIEEE/ASME Intl Conf. on Advanced Intelligent Mechatronics, 8-12 July 2001, vol. 2, pp.1011-1016.ABOUT THE AUTHORSAssoc. Prof. Radi Romansky, PhD, Department of Computer Systems, TechnicalUniversity - Sofia, Phone: +359 2 965 32 95, e-mail: firstname.lastname@example.com Parvanova, PhD Student in Computer Systems Dept., Technical University Sofia, e-mail: firstname.lastname@example.com
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e-Learning09Table 1 - Differences between CSCW, CSCL and CSCR (apud [11])CSCWFocuses oncommunicationtechniquesUsed mainly in abusinesssettingPurpose is tofacilitate groupcommunication andproductivity
CSCLFocuses on what isbeingcommunicatedUsed mainly in aneducational setting
CSCRFocuses on newcommunications
Purpose is tosupportstudents in learningtogether
Purpose is to supportresearchers in workingtogether
Used mainly in aresearch setting
e-Learning09Training Committee), IMS (Instructional Management Systems) and IEEE (Institute forElectrical and Electronics Engineers / Learning Technologies Standards Committee),allowing the creation of documents with unique and unitary content. [7]In order to provide the basis for analyzing collaborative authoring tools it is helpfulto identify the most important functional requirements for such tools, taking into accountthe way they should support the process of collaborative authoring. Beside the actualediting of documents, a lot of activities are going on in the authors group, in order toensure a common view of the developed result.The typical requirements for a collaborative authoring tool are: support for different documents formats; the possibility to make comments connected to specific parts of the document; concurrent editing of the document: the tools should guarantee the consistencyof a document which is simultaneously changed by a number of authors;eventual conflicts should be managed; revision management, i.e. the possibility to track the changes and to maintainversions / revisions of a document; communication support within the group, either synchronous or asynchronous,voice- or text-based; group membership management in a dynamic way throughout the collaborativeactivity; organization tools, like calendars and task lists, for easy coordination of thecommon and individual activities. capturing unstructured information, like notes, sketches etc.This list is used in the following for a short characterization of specific tools.A COMPARISON OF SELECTED TOOLSNowadays there is a broad range of software tools which include relevant functionsfor collaborative authoring; in the following, a selection of tools to be analyzed is made,including the most well-known ones, both commercial and open-source / free. Theseare described shortly, and a comparison table is elaborated.Google Docs is an online collaborative platform for creating and editingdocuments. Several document types are implemented: text documents, spreadsheets,presentations and PDF files. These can be edited fully online through Google Apps,without the need for an office-like software on the client side. There are size limitationsfor the documents, e.g. 500 KB (without the pictures) for text documents, or 10 MB forPDF files.The documents can be shared with other Google users, which are specified eitheras "collaborators" or "viewers". The platform supports simultaneous editing and viewingby the collaborators. The versions of text documents are maintained as "revisions",which can be visualised and describe the change and the user who made it. Thedifferent revisions can be compared for easy identification of the changes.Google Docs does not include other facilities like discussions, instant messaging,task planning or calendars, but these are available through other specialized Googleservices. The disadvantage is that the "collaboration workspace" is not defined andcentrally managed, so the users have to navigate in turn to the necessary pages.The Microsoft Word text editor supports several functions for collaborativeauthoring, such as: tracking changes, inserting comments and maintaining documentversions. "Track changes" can be activated, having as effect the highlighting of changesmade in the document (both content and formatting). The changes are colored andrelated to the author. There are further possibilities to accept or reject a change whengenerating the final form of the document.Comments can be inserted and linked to a part of the document, in order to- 285 -
e-Learning09provide the collaborators with additional information related to that part. The commentsinclude the author identification.Different versions of a document can be saved in an explicit way. The authors arethen able to access both the current version or a previous one.The Microsoft Word editor is integrated with other Microsoft product, likeSharePoint or Office Groove, in order to facilitate the sharing between authors in acommon workspace.The Microsoft Office Groove collaboration application is part of the MicrosoftOffice product family. It is a desktop software application that enables the creation ofshared "workspaces", which can be flexibly composed of different "tools". The FilesTool, a basic file repository for sharing documents, is the most relevant one forcollaborative authoring purposes. The Discussion, Calendar and Notes tools can offeradditional support for collaborative work. The authors need to have the softwareinstalled locally and to create a Groove account; then they are able to create a newworkspace or join an existing one, based on an invitation.The workspace window shows the documents which are new or modified by otherusers as unread, helping the user to identify the changes since the last working session.There is a facility for simultaneous navigation of the users in the workspace, which ishelpful when the users are communicating directly (e.g. via Instant Messaging ortelephone). The local workspaces of the users are synchronized by means ofbackground data communication.The workspace includes activity indicators of the other users (called "contacts")and has functionality for instant messaging and voice communication. More structureddiscussions can be hosted in the Discussion Tool.Microsoft SharePoint is a web-based content management system whichenables the creation of collaboration websites through the combination of "web parts".The installation and configuration effort is significant: the SharePoint Server (orSharePoint Services, which offers a subset of functionalities) has to be installed, thewebsite has to be configured and the users must be defined by an administrator. Thismakes SharePoint an option mostly for larger organizations which want to provide theirworkers with a way of collaborating online.SharePoint includes functionalities for document management, web contentmanagement and business processes (workflows), as well as enterprise search andreporting facilities. Collaborative work facilities include shared document libraries andcommunication tools like wikis, discussion boards etc. The documents in a documentlibrary can be modified concurrently; the users perform explicit check-out/check-inoperations when they open a document for editing. A "document workspace" can becreated for each document that is written in collaboration. This is a sub-site withfunctionalities for tasks, discussions, announcements and links. The documentmanagement facilities include document versioning.The most known SCORM-based authoring platform used for creation ofdocuments with collaborative content is SELF, acronym for Science, Education andLearning in Freedom [8]. SELF represents a web-based platform containing multilanguage documents written collaboratively by experts and interested users. Its mainobjective is to bring together public institutions like universities, governments,regardless of their profile or country, providing tools for the evaluation, adaptation andtranslation of electronic documents it contains. This way, SELF provides support fordocuments presented in different languages and forms: plain text, presentations,software tutorials, e-books, videos and manuals. The collaborative authoring process forthese resources is based on the organisational model of Wikipedia combined with theguarantee that the content is scientifically and academically correct, through qualityassurance mechanisms [8].Mindquarry represents an open source collaborative platform which provides- 286 -
e-Learning09sharing of different types of files (documents, images, video and audio files), organizinginformation, e-mail integration, wiki editing and forums. Initially, the platform wasdesigned as a web application but now it also has a desktop client which facilitates filesynchronization and the work offline. Mindquarry could be used as alternative toSharePoint because of its user and task management features, allowing users toconnect from anywhere and to change and synchronize information, thus increasing theefficiency of the teams using the platform. [9]Moodle (Modular Object-Oriented Dynamic Learning Environment) is anothercollaborative platform, based on social-constructivism principles. The basic idea forsocial-constructivism is that the students learn more when they have to createdocuments and materials that they have to explain to other persons. For instance, it iseasy for someone to read a text, but someone can understand a text better if it tries toexplain it to somebody else [10]. Moodle encourages communication between authorsthrough its communication and collaboration tools, like chat rooms, forums, wikis andreviews which can be edited using the embedded WYSIWYG editor. Also, Moodle canexport and import documents according to SCORM and IMS standards.Etherpad is fully web based solution for creating collaborative content in real time.It does not provide functionalities for uploading or saving documents, but it allows up to8 people write at the same time on the same article by simply accessing the link of thearticle, stored on Etherpad server. Thus, it has a greater portability, allowing peoplehaving different operating systems to collaborate. Etherpad is the only authoring toolallowing authors to see in real time the changes made to the document by the otherauthors, by highlighting text according to the user that changed it. Also, Etherpad keepsthe entire history of the documents so that each change made to the document can beundone no matter when it was made. [3]The findings of the above analysis are summarized in Table 2.Table 2. Summary of characteristics of collaborative authoring toolsTool
CharacteristicsLicense type
Google Docs
Web-based /Desktopfully web-based
MS Word
MS OfficeGroove
web-based(except documentediting)web based
web based
free /commercial
Web based ,optional desktopclient
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Functionality- synchronous editing, sharing, comments,revisions- other functions as separate services- text editing, comments, tracking of changes,versioning- file repository, messaging, discussions,calendar, notes- group membership, read/unread status ofitems- document library, check-in/check-outmechanism, versions- discussions boards, wikis, calendars- management of documents in collections,secure web server, content managementsystem- shelf management, internationalization- site, user and course management;- embedded WYSIWYG HTML editor, mailintegration, file management, chat, wikis- real time editing, highlighted authorchanges- chatting, syntax highlighting for editing code- file synchronization, tracking of changes,wiki editor, RSS feed- user and task management
e-Learning09CONCLUSIONSThere is a broad range of available tools for collaborative authoring, which differsignificantly in terms of architecture and functionality. Most of these tools are covering alarger area of CSCW and are not restricted to collaborative authoring only; therefore,the extent to which the specific requirements of collaborative authoring are supported isalso very different. It would be helpful for the end-user that a standard set offunctionalities/requirements is defined, as a basis for a consistent characterization ofsuch tools.REFERENCES[1] Bordin Sapsomboon, Restiani Andriati, Linda Roberts and Michael B. SpringSoftware to Aid Collaboration: Focus on Collaborative Authoring, 1997, School ofInformation Sciences University of Pittsburgh, USA[2] David Megas1, Jonas berg2, Wouter Tebbens3, Rafael Macau - SELF: acollaborative platform to share and create free content about free software and openstandards, PKP Scholarly Publishing Conference, July 11 - 13, 2007[3] Etherpad, http://etherpad.com/, last accessed 16.07.2009[4] Gerry Stahl - Group Cognition: Computer Support for Building CollaborativeKnowledge, MIT Press, 2006[5] Gerry Stahl Groupware Goes to School, in the volume Groupware: Design,Implementation, and Use, Springer, 2002[6] Kevin Keenoy, Mark Levene, Sara de Freitas, Mrta Turcsnyi-Szab, EszterBodnr, Bruno Emans, Judith Schoonenboom, Jean-Philippe Pernin, Carole Eyssautier,Ann Jones, Andrew Brasher, Jenny Waycott, Lydia Montandon - Collaborative trails ine-Learning environments, Kaleidoscope Deliverable D22.4.2, Dec. 2004,http://eprints.bbk.ac.uk/197/1/keenoy2.pdf[7] Liu X, El Saddik A., Georganas, N. - An Implementable Architecture Of An ELearning System, Electrical and Computer Engineering, Volume: 2, p. 717- 720, 2003,ISSN: 0840-7789[8] Meena Kharatmal- SELF Platform A Teacher-Centric Collaborative AuthoringSystem, National Conference on Open Source Software, NCOSS 2009, 25-26 may2009, Navi Mumbai, India[9] Mindquarry, http://www.mindquarry.com/publications/articles, last accessed16.07.2009[10]MOODLE,http://docs.moodle.org/en/About_Moodle,lastaccessed16.07.2009[11] Vita Hinze-Hoare - Computer Supported Collaborative Research, Nov. 2007,http://arxiv.org/ftp/arxiv/papers/0711/0711.2760.pdfABOUT THE AUTHORSProf. Dr. Constantin Apostol, Department of Business Informatics, Academy ofEconomic Studies Bucharest, Phone: +40-21-319-1900/302, E-mail: email@example.com. Dr. Iulian Intorsureanu, Department of Business Informatics, Academy ofEconomic Studies Bucharest, Phone: +40-21-319-1900/313, E-mail: firstname.lastname@example.com. Dr. Rodica Mihalca, Department of Business Informatics, Academy ofEconomic Studies Bucharest, Phone: +40-21-319-1900/313, E-mail: email@example.com. Alina Mihaela Ion, Department of Business Informatics, Academy ofEconomic Studies Bucharest, Phone: +40-21-319-1900/302, E-mail: email@example.com
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INTRODUCTIONThe purpose of the present study is to determine the founding moments inapproach for creation and development of virtual laboratory for education, mathematicalmodeling and computer simulations in the scientific researches in the field of forestry,agronomy and veterinary medicine.Laboratory will be established as specialized research section within the structureof University of Forestry, Sofia (UF) with main goal research, development andimplementation activities for research, application and development of complexscientific and educational innovations in the field of multifunctional usage of forestryresources, in the stable agriculture growth, plant protection and veterinary medicine.The laboratory will execute tasks concerning analysis, development and transfer oftechnologies for virtual education and virtual modeling in the scientific experiments inexpedient interdisciplinary scientific fields.PROBLEMS AND CURRENT STATE OF VIRTUAL LABORATORIES INEUROPEAN SPACEIntegration of modern technologies for education and perspective information andcommunication solutions in the national educational system is most effective withinunified educational space and means reorganization of both content and organizationalforms of educational activities, creation of modern means for information-technologicalsupport and development of both educational and research processes.Realization of such strategy requires creation, within the educational institution (ornetwork of institutions), of integrated information platform for education, based onspecialized program-instrumental means and educational content, which could bedefined as information-resourceful or rich in content virtual platform for education andinnovations in the scientific researches.Educational platform of modern educational systems is formed from cooperationbetween traditional and innovational learning models, systems of standards, integratedcontent of learning plans and programs, educational means and materials, as well asnew interaction quality, dialogical and corporate communication between theeducational subjects. Creation of mobile information-educational platform, based onmodern information and telecommunication technologies, guarantees principally newlevel of access to quality education.One of leading aims of high university degree specialists preparation is their abilityto produce and implement innovations, for which they should possess adequateknowledge, to be familiar with the technology for discovering novelties and theirimplementation. University education is connected to generation of new knowledgemost of all, their management, as they are key components of the innovational process.It is important to analyze whether form virtual laboratory for education andinnovations in the scientific researches is being applicable currently and if it has- 289 -
e-Learning09potential and perspective. Analysis reveals, that such forms are up to date and used inEurope and Bulgaria. Brief analysis below illustrates the accumulated experience.University of Rousse - Angel Kanchev: Virtual laboratory in electronic educationin discipline Computer organization; Virtual laboratory in physics offers to studentsgroup of virtual exercises from all fields of physics.Medical University Sofia: Web-based course; enhances the traditional educationin biochemistry with interactive simulations of clinical cases, virtual laboratory andvisualization of complicated processes.Technical university Sofia: Laboratory and center for Virtual engineering. Aconnection is expected, between system for virtual reality in TU Sofia and newlyfounded center for technological engineering solutions within University of Karlsruhe,Germany for combined projects and cooperation; Virtual laboratory within Faculty oftelecommunication technique and technologies, part of global project vLab Virtuallaboratory for Automated systems for measurement and control.University of Chemical Technology and Metallurgy Sofia: Virtual laboratory inapplied mechanics.European Virtual laboratory in mathematics (EVLM) http://evlm.stuba.sk/.University of Poznan Vlab: Architecture of Virtual Laboratories(http://vlab.man.poznan.pl/eng/).University of Oulu, Finland http://vlab.oulu.fi/eng/index.htmlInsitut fr technische chemie, Universtt Leipzig An Internet-Based LaboratoryCourse in Chemical Reaction Engineering and Unit Operations;Alcal University, Madrid Internet Based Laboratory for Experimentation withMultilevel Medium-Power Converters.The above stated examples show, that creation of Virtual laboratories is anongoing practice in Europe and Bulgaria and is supported by research programs ofEuropean commission. Laboratories integrate the efforts of interdisciplinary teams ofscholars.APPROACH FOR CREATION OF VIRTUAL LABORATORYVirtual laboratory is intended to combine, based on the program-goal principle, thescientific and educational capacity and resources of UF for researches and creation ofhigh-end technologies for virtual modeling and education in the field of forestry andforest industry, landscape architecture, agronomy, plant protection and veterinarymedicine.In the work of virtual laboratory will be involved lecturers from Department ofComputer Systems and Informatics, as well as leading lecturers and scientists fromUniversity of Forestry.For completing its tasks and goals, vLAB will be used and will assist thedevelopment of information and technological structure of the university as a generalvirtual platform for education and development of scientific researches in priorinterdisciplinary fields:9 stable usage and development of biological resources in forestry, agronomyand veterinary medicine;9 application of GIS-technologies and modern distance methods forprotection, monitoring and reproduction of environment;9 stable management and development of forestry sector, agriculture andplant protection, veterinary medicine.The approach for creation and development of virtual laboratory for education,mathematical modeling and computer simulations in scientific researches in the field offorestry, veterinary medicine, agronomy and plant protection is based on complexsystem for interdisciplinary scientific researches and education, including virtual and- 290 -
e-Learning09distant experimental components, interactions between courses, seminars and testingsystems.The task is to analyze the possibilities of virtual reality (VR technology) andsimulation modeling for assisting the education and support of the scientific researchesin above mentioned interdisciplinary fields.Forestry and its compatibility with VR. Forestry is a field, where precision of thescientific prognosis and the effectiveness of the management strategy are essentialparts of success. Temporal frames, within which the forest systems react on performedevents, are wide. More and more often, scientists turn to computer tools for modelingand visualization, for projecting the changes in the forest and estimation of theirinfluence. Not long ago, visualizations were used mostly for modeling the effect of theglobal ecological changes, for estimation of yields, for visualization of different featuresand changes in the forest or for visualization of landscape. The importance ofvisualization in forestry is obvious. The need of visualization tools is growing, inparticular for such instruments that could model the future forest resources andvisualize dynamics of the forestry processes and ecosystems in time. This determinesthe increased attention to VR in forestry decision making.VR is particularly suitable for assistance in solving problems in the following fieldsof forestry: temporal dependence, decisions irreversibility, spatial-volume modificationsand multifunctional forest management.Interactive program/simulation/ models and applications in veterinarymedicine. Veterinary profession requires precise estimation and dynamic decisionmaking. Learning is based on approach related to monitoring and repeating. Simulationbased approaches provide option for multiple repeating of observations till sufficientlevel of experience is reached. Fields of veterinary medicine in which simulation modelsin education are created and used are as follows: animal, fish and birds feeding,histology, pathology, radiology, surgery, anesthesiology, medicine dosage, clinicallaboratory, meat hygiene. Application of simulation models in virtual laboratory assumesstudents to gather and analyze data on screen in such way that can be done withtraditional equipment; realization of educational activities related to answering questionswith one or more possible answers for testing of factological knowledge; exercises,related to analyses and data interpretation.Implementation of multimedia learning, in which various situations are analyzed,present expert decisions, allows development of skills for thinking and decision taking inobservation of several perspectives and availability of more than one possible ways tosolve the problem. Setting up active knowledge in students becomes possible withintegration of situational, strategic and full of matter knowledge.Geographical information systems (GIS) and their implementation inagronomy and plant protection. GIS is a software pack and hardware designed tocollect, store, update, analyze and visualization of information, connected withgeographical location of the objects, for which it stands for. This type of information isrealized through maps, geographical data and models for data analyses. Modern GISprovide opportunity, information for current nature and social phenomena in all fields ofhuman activities, related to geographical alteration, to be permanently actualized andprovided to users for analyses and decision taking in processes management.Computer technologies and contemporary aeronautic devices for observation of Earthprovide possibility for real time monitoring and detection of variation in Earth surface,which are result from nature phenomena and human interference. That is why GIS isbecoming popular in the area of agricultural resources management, especially inagronomy and plant protection.Increasing in yield volumes is the major goal for achieving success in- 291 -
e-Learning09contemporary competitive agricultural resources market. This situation requires growersto look for ways to increase the yields from the available lands under crops. One of theapproaches for achieving effective results is the proper organization of the land fund inthe agriculture, aiming achieving of effective cyclic recurrence in agronomictechnologies for support and ecological self recovery of the soils.Implementation of GIS-based models in learning process provides opportunity forachieving analytic skills, planning and management of agricultural activities, related tohydro melioration events, treatment with agrochemicals and fertilizers, fighting plantdeceases and agricultural pests, based on structure information for the current andprognoses status of geographical characteristics (soil, landscape and climateconditions) of the cultivable area.PRACTICAL STUDY OF POSSIBLE INSTRUMENTS FOR VIRTUALLABORATORYThe approach for creation of virtual laboratory, includes examination of simulationand visualization tools and instruments in the field of streaming media as well asestimation of there possibility.In the present investigation were researched and analysed following simulationand visualization instruments:Forsi-simulator product of the Finnish company Plustech. It is with high level ofprecision in representing the forest landscape, with marking structures on the map, butdoes not allow movement or interaction with the basic objects;UVIEW designed by Robert J. McGaughey from USDA Forest Service, PacificNorthwest Research Station computerized graphic representation, which picture theexisting or expected landscape conditions. System offers flexible tools for analyses;Monsu developed by Timo Pukkala, University of Joensuu, initially has beendesigned for multifunctional forest planning, but also includes high visualization abilities;SmartForest developed by Imaging Systems Laboratory, University of Illinois, incooperation with USDA Forest Service and University of Helsinki. Contains advancedinstruments for moving and interaction within forest area.World Construction Set, Visual Nature Studio and Scene Express developed by3D Nature LLC. Provides options for realistic views of real nature objects and places,using vector data DEMs/DTMs and information from remote sensors. It can model longterm forestry planning, underground flows, ecological projects and recreationalscenarios. Allow creation of maps, plans, stereo panoramas and flight. It can modeldisaster areas and show their progress.The realistic view is possible through the option for setting digital data for leaves,clouds, mist, water, reflection, sun, sun rays, etc. The software allows modeling ofobjects alteration in time from one day to million years. Possible integration withArcView/ArcInfo, ERDAS Imagine, ERMapper, AutoCAD, MicroStation, 3D StudioMAX/Viz, LightWave, etc.The above mention instruments can complement different developments in thefield of simulation modeling, based on scientific researches in university.Some of the possibilities for high quality services connected to streaming mediaare:HELIX SERVER represents solution for acquiring streaming media with highquality. General advantages of this solution are acquiring of streaming resources indifferent audio and video formats, optimized ratio of investments to number of serviceusers, and also possibilities for flexible politics towards using new formats. Observationsshow that solution fits well in heterogenic environment and allows decrease of expensesrelated to using different devices.Another suitable solution for streaming media is FLASH MEDIA INTERACTIVESERVER 3. Its advantage is the option to combine access to streaming media of large- 292 -
e-Learning09number of users, with flexible and functional platform for creation and processing ofstreaming content. Also interesting are the possibilities for security management of thestreaming media considering their classification based on access rights, broadcasting inreal time and supply of content to mobile users, using phones with Adobe compatiblesoftware. This solution helps for effective usage of possibilities of the networkinfrastructure to supply content, as well as optimizing the process in accordance to theclient possibilities to work with the streaming media.As for decision choice, solutions with open source can be used, for exampleDarwin Streaming Server. This server for streaming media represents solution based onApple QuickTime Streaming Server and is using standardized protocols for supply ofstreaming media. Solution can be realized on different platforms, allowing change ofoutsource depending on specific needs, but on the other side assumes availability ofwell prepared IT and communication technology unit.Microsoft Windows Media Services 2008 is free of charge as part of serveroperational system Windows. Solution is suitable for clients, using actively Microsoftproducts within their intra- and extranet networks, as well as internet services.Advantages of such solution must be found in opportunity for combining streamingmedia services with other network services, as well as many means for developmentactivity. This can contribute for creation of new quality applications, combining differentexisting technologies and approaches.CONCLUSIONS AND FUTURE WORKVirtual laboratory provides opportunity for alteration of parameters of theresearched objects, characteristics and scale of surroundings, construction of which istoo complicated to be realized in the real physical experiment. Preservation of thecreated model with option for future usage and reproduction of the experiment underchanged parameters and conditions will accelerate scientific researches.Virtual laboratory will provide hypermedia platform for delivering all types ofinformation text, graphics, audio and video, which will ease and intensifycommunication between young scientists, PhD students and post-docs. Result of thescientific researches will be available anytime and from any place with access toInternet, which provides flexibility, dynamics, interactivity of communications betweenthem and new opportunities for their development.REFERENCES[1] Aron, D, Developing multimedia-based case training for small animalorthopedic surgery. University of Georgia, USA, 2005.[2] Brown, T, Daniel, T. Predicting scenic beauty of timber stands. For. Sci. 32(2):1986.[3] E-learning and simulation labs. HEEPF deliverables. http://www.heepf.org.eg[4] E-learning project Fish in the Web. Biology and pathology of fish. 2005. Elearning academy Zagreb.[5] Emergency case simulator. Royal veterinary college, University of London.http://www.rvc.ac.uk/review/cases/index.htm[6] Heasley, J., McNamara, J. Simulating the visual effects of timber harvestpractices. In Proc. Resource Technology 90 - Second Symposium on AdvancedTechnology in Natural Resource Management. Washington, D.C., American Society forPhotogrammetry and Remote Sensing, Bethesda, Maryland, 1990.[7] http://3dnature.com/[8] Kellomki, S., Pukkala, T. Forest landscape: A method of amenity evaluationbased on computer simulation. Landscape Urban Plann., 1989.[9] Magee, M. Simulation in education. Final report. Alberta online learningconsortium, Calgary AB, 2006.- 293 -
e-Learning09[10] 2008 2010., .[11] Orland, B., LaFontaine, J., Daniel, T. Alternative Futures for ForestedLandscapes. In Proc. Resource Technology 90, Washington D.C. American Society forPhotogrammetry and Remote Sensing, Bethesda, Maryland, 1990.[12] Orland, B., Daniel, T., Lynch, A., Holsten, E. Data-driven Visual Simulation ofalternative Future for Forested Landscapes. In proc. IUFROIntegrating ForestInformation over Space and Time. Canberra, 1992.[13] Orland, B. Smartforest: 3-D interactive forest visualization and analysis. InProc. Decision Support-2001, Resource Technology 94, Toronto. American Society forPhotogrammetry and Remote Sensing, Bethesda, Maryland, 1994.ABOUT THE AUTHORSAssoc.Prof. Boyanka Zhelyazova, PhD, Department of Computer Systems andInformatics, University of Forestry - Sofia, Phone: +359 91 907 338,-mail: email@example.com. Radoslav Miltchev, PhD, Department of Computer Systems andInformatics, University of Forestry - Sofia, Phone: +359 91 907 340,-mail: firstname.lastname@example.com.
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INTRODUCTIONIn the educational process students assimilate various parts of the subject matter.Therefore its very important to manage this process. The more efficient it is the biggerpart of the subject is assimilated. According to an author the level of the student trainingis changing continuously [4]. Because of this, different important characteristics of theknowledge dynamic will be researched.The educational process is very important and so is his efficiency. In connectionwith that quantity evaluations of the basic characters have to be created. Design of themacrographs of Petri nets has been offered to make the evaluation process easier. Thegrouping macro positions correspond to the separate themes that construct the relevantsubject.The purpose of this paper is to offer an easy accessible way for gatheringimpressions about the quantity dimension of the educational process and discoveringpotentional opportunities for increasing its effectiveness.QUANTITY CHARACTERISTICS OF THE KNOWLEDGE DYNAMICThe analysis of the quantity characteristics of the knowledge dynamic allows themanagement of the school process to be optimized. The management optimization isexamined this way: assimilation, forgetting and repeating of the school material areforming a casual process. The space of its realizations can be characterized with acertain possibility measure. To take into account the priorities of separate obligatory oradditional terms in assimilation of certain topics, weight coefficients of the arcs in thePetri nets showing the school process on this topic are initiated. The next stagerepresents the learning of several topics from the school material in one school subject.Determining the steps in learning the topics must take into account the succession ofsome knowledge parts from one topic to another. This forces the introducing of onemore kind of weight coefficient, which is the volume of inherited knowledge from theprevious to current topics. The character of these weight coefficients is probability, ofcourse. Because of the condition that it is obligatory to assimilate a term during a givenstage from a previous topic, these possibilities can be only conditional.Each combination of realizations of the casual process and the correspondingcontrol have a quality parameter (stage of usefulness). Therefore, the task foroptimization of the school process management is reduced to syntheses of the strategyof obligatory process management, which can guarantee extreme value of the qualityparameter.
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e-Learning09ASSIGNMENT THE PERFORMANCES OF A HOMOGENOUS CLOSED NET,DESCRIBING THE KNOWLEDGE DYNAMICThe homogenous closed net describes studying on a topic from a current schoolsubject. Such system is previewed in [2] and the corresponding Petri net /with weightfactors of the arcs/ is given on Figure 1.
Fig.1With is registered the current number of the terms that have to be assimilated.The coefficient with first index 2 has to be with lowest possible value for the mostobligatory term that has to be assimilated. Its value increases with decreasing theimportance of the term in this school subject. Assigning values to the coefficients withfirst index 1 is in a reciprocal way, because its about an opposite process forgetting.With and are registered respectively the probabilities for once more assimilatingand for forgetting the terms that have to be assimilated. To ensure correct inputconditions to working the net instead of the values of r, and its necessary only amark to be marked in all positions from p7 up.(1,2,0,0,0,0,0,1,1,...,1)t1(1,2,0,0,0,0,0,1,1,...,1)t2(1,2,0,0,0,0,0,1,1,...,1)t2(1,2,0,0,0,0,0,1,1,...,1)t3(1,2,0,0,0,0,0,1,1,...,1)
t6+nt5 t6 .
Fig.2.On Figure 2. the reachability tree for the status when r=2 and the initial marking is0=(1,2,0,0,0,0,0,1,1,...,1). is shown.In the net analyses for evaluation of the knowledge dynamic usage of thenormalizing coefficient is also recommended. For opened nets this constant has asimple layout, and for the closed nets because of the complexity of the knowledge- 296 -
e-Learning09dynamics it is the sum of a product. The number of the addends in this sum is equal tothe dimension of the status space of the net. For example, for the case of homogenousclosed net the size H is:M-1
e-Learning09Let there be two classes of priorities 1 and 2, and the terms from the first classare with higher priority than these from the second class (the lower numbercorresponding to a higher). Besides, the first class consists N1 terms, but the second N2. Therefore, the total number of terms is N = N1 + N2. In the terms of Petri nets thiswill lead to realization of this branch of the net, which arc possesses a lower weightcoefficient.If we examine a case of a two class system, then when a term with higher priorityfrom first class is input, the assimilation of the lower priority, can be cancelled (this is theso called absolute priority), and to be put in the beginning of the row with terms from thesecond class.The student can be in one of (N1 +1)(N2 +1) i,j statuses,where (i=0,N1, j=0, N2),and i,j is a status, when the student doesnt know i number of terms from the first classand j number of terms from the second class.If z11 and z12 represent the intensity of the forgetting term from first and secondclass respectively, and z21 and z22 represent the intensity of the knowledge recoveringrespectively, then the change in the student knowledge can be represented with amarkov process (in zij the first index i shows the stage number, and the second j the term class number). From here a schedule for the student transitions from onestatus to another can be composed, and on it to compose a differential equationsystem. From this system there can be found, for example, the stationary possibilities lijFrom this system, for example, the stationary probabilities lij for staying of thestudent into different situations are expected to be found. Later, quantity characteristicsabout the quality of the training can be found.If the mathematical expectation about the number of the forgotten terms in the firstclass is L1, and the second one L2 , than for all the forgotten terms we get L = L1 + L2.The intensity of the terms streams who enter and exit the S1 stage are equal. As aresult, the average amount of the terms processing during the S1 stage can be found.CONCLUSIONSThe advantage of using Petri net is the opportunity of analyzing the systemsbehavior and researching it.The quantity valuation of the characters for the quality of the teaching process hasalways been serving to determinate the students position depending on the level of theassimilated knowledge. On the other side, it can also serve to show the direction thatthe work must be done to, in order to correct or to upgrade the level of the studentsqualification. That is why he can be continued to be taught or not.REFERENCES[1] Filipova, K., Sv. Stefanova, Defining the Performances of the KnowledgeDynamic Models, Annual School.Lectures- 2003, 3-5, vol.23, number 1, Sept2001/2003.[2] Filipova, K., Sv. Stefanova, Net Models of Knowledge Dynamic, AnnualSchool.Lectures- 200,; 29-32, vol.23, number 1, Sept 2001/2003.[3] Stefanova, Sv., K. Filipova, I. Zheliazkova, A Technology for Teaching byMeans of an Intelligent Animation Environment, Annual School.Lectures- 2003, 17-19,vol.23, number 1, Sept 2001/2003.[4] Sviridov A.P., I.A. Shalobina, Net models of knowledge dynamic, Moskva, MEI,1992.[5] Yulin Y. A Petri net model for temporal knowledge representation andreasoning, IEEE. Trans. On Systems, Man. And Cybernetics, v. 24, 9, 1994.
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e-Learning09[6] K., . , , First International Conf. Challenges in HigherEducation in the 21st Century, pp.48-50, may, Sozopol, 2003ABOUT THE AUTHORSSvetlana Stefanova, PhD, Department of Computer Systems, University ofRousse, Phone: +359 82 888 356, Email: email@example.com Filipova, PhD, Department of Automatic, University of Sofia,Phone: +359 02 9652526, Email: email@example.com
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INTRODUCTIONThe paper organized as following: in the second chapter we describe briefly theselected knowledge management approach. The third chapter is devoted to thesoftware engineering body of knowledge. The next chapter represents the<