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An intelligent tutoring system (ITS) is a computer system that aims to provide immediate and customized instruction or feedback to learners,[1] usually without intervention from a human teacher. ITSs have the common goal of enabling learning in a meaningful and effective manner by using a variety of computing technologies. There are many examples of ITSs being used in both formal education and professional settings in which they have demonstrated their capabilities and limitations. There is a close relationship between intelligent tutoring, cognitive learning theories and design; and there is ongoing research to improve the effectiveness of ITS.
History of ITSEdit
Early mechanical systemsEdit
The possibility of intelligent machines have been discussed for centuries. Blais Pascal created the first calculating machine capable of mathematical functions in the 17th century simply called Pascal's Calculator. At this time the mathematician and philosopher Leibniz envisioned machines capable of reasoning and applying rules of logic to settle disputes (Buchanan, 2006).[2] These early works contributed to the development of the computer and future applications.
The concept of intelligent machines for instructional use date back as early as 1924, when Sidney Pressey[1] of Ohio State University created a mechanical teaching machine to instruct students without a human teacher.[3] His machine resembled closely a typewriter with several keys and a window that provided the learner with questions. The Pressey Machine allowed user input and provided immediate feedback by recording their score on a counter.[4]
Pressey himself was influenced by Edward L. Thorndike, a learning theorist and educational psychologist at the Columbia University Teacher College of the late 19th and early 20th centuries. Thorndike posited laws for maximizing learning. Thorndike's laws included the law of effect, the law of exercise, and the law of recency. Following later standards, Pressey's teaching and testing machine would not considered intelligent as it was mechanically run and was based on one question and answer at a time,[4] but it set an early precedent for future projects. By the 1950s and 1960s, new perspectives on learning were emerging. Burrhus Frederic "B.F." Skinner at Harvard University did not agree with Thorndike's learning theory of connectionism or Pressey's teaching machine. Rather, Skinner was a behaviourist who believed that learners should construct their answers and not rely on recognition.[3] He too, constructed a teaching machine structured using an incremental mechanical system that would reward students for correct responses to questions.[3]
Early electronic systemsEdit
In the period following the second world war, mechanical binary systems gave way to binary based electronic machines. These machines were considered intelligent when compared to their mechanical counterparts as they had the capacity to make logical decisions. However, the study of defining and recognizing a machine intelligence was still in its infancy.
Alan Turing, a mathematician, logician and computer scientist, linked computing systems to thinking. One of his most notable papers outlined a hypothetical test to assess the intelligence of a machine which came to be known as the Turing test. Essentially, the test would have a person communicate with two other agents, a human and a computer asking questions to both recipients. The computer passes the test if it can respond in such a way that the human posing the questions cannot differentiate between the other human and the computer. The Turing test has been used in its essence for more than two decades as a model for current ITS development. The main ideal for ITS systems is to effectively communicate.[4] As early as the 1950s programs were emerging displaying intelligent features. Turing's work as well as later projects by researchers such as Allen Newell, Clifford Shaw, and Herb Simon showed programs capable of creating logical proofs and theorems. Their program, The Logic Theorist exhibited complex symbol manipulation and even generation of new information without direct human control and is considered by some to be the first AI program. Such breakthroughs would inspire the new field of Artificial Intelligence officially named in 1956 by John McCarthy in 1956 at the Dartmouth Conference.[2] This conference was the first of its kind that was devoted to scientists and research in the field of AI.
The latter part of the 1960s and 1970s saw many new CAI (Computer-Assisted instruction) projects that built on advances in computer science. The creation of the BASIC programming language in 1958 enabled many schools and universities to begin developing Computer Assisted Instruction (CAI) programs. Major computer vendors and federal agencies in the US such as IBM, HP, and the National Science Foundation funded the development of these projects.[5] Early implementations in education focused on programmed instruction (PI), a structure based on a computerized input - output system. Although many supported this form of instruction, there was limited evidence supporting its effectiveness.[4] The programming language LOGO was created in 1967 by Wally Feurzeig and Seymour Papert as a language streamlined for education. PLATO, an educational terminal featuring displays, animations, and touch controls that could store and deliver large amounts of course material, was developed by Donald Bitzer in the University of Illinois in the early 1970s. Along with these, many other CAI projects were initiated in many countries including the US, the UK, and Canada.[5]
At the same time that CAI was gaining interest, Jaime Carbonell suggested that computers could act as a teacher rather than just a tool (Carbonell, 1970). A new perspective would emerge that focused on the use of computers to intelligently coach students called Intelligent Computer Assisted Instruction or Intelligent Tutoring Systems (ITS). Where CAI used a behaviourist perspective on learning based on Skinner's theories (Dede & Swigger, 1988),[6] ITS drew from work in cognitive psychology, computer science, and especially artificial intelligence.[6] There was a shift in AI research at this time as systems moved from the logic focus of the previous decade to knowledge based systems—systems could make intelligent decisions based on prior knowledge (Buchanan, 2006).[2] Such a program was created by Seymour Papert and Ira Goldstein who created Dendral, a system that predicted possible chemical structures from existing data. Further work began to showcase analogical reasoning and language processing. These changes with a focus on knowledge had big implications for how computers could be used in instruction. The technical requirements of ITS, however, proved to be higher and more complex than CAI systems and ITS systems would find limited success at this time.[5]
Towards the latter part of the 70's interest in CAI technologies began to wane.[5][7] Computers were still expensive and not as available as expected. Developers and instructors were reacting negatively to the high cost of developing CAI programs, the inadequate provision for instructor training, and the lack of resources.[7]
Microcomputers and intelligent systemsEdit
The microcomputer revolution in the late 1970s and early 80s helped to revive CAI development and jumpstart development of ITS systems. Personal computers such as the Apple 2, Commodore PET, and TRS-80 reduced the resources required to own computers and by 1981, 50% of US schools were using computers (Chambers & Sprecher, 1983).[5] Several CAI projects utilized the Apple 2 as a system to deliver CAI programs in high schools and universities including the British Columbia Project and California State University Project in 1981.[5]
The early 80s would also see ICAI and ITS goals diverge from its roots in CAI. As CAI became increasingly focused on deeper interactions with content created for a specific area of interest, ITS sought to create systems that focused on knowledge of the task and the ability to generalize that knowledge in non-specific ways (Larkin & Chabay, 1992).[6] The key goals set out for ITS were to be able to teach a task as well as perform it, adapting dynamically to its situation. In the transition from CAI to ICAI systems, the computer would have to distinguish not only between the correct and incorrect response but the type of incorrect response to adjust the type of instruction. Research in Artificial Intelligence and Cognitive Psychology fueled the new principles of ITS. Psychologists considered how a computer could solve problems and perform 'intelligent' activities. An ITS programme would have to be able to represent, store and retrieve knowledge and even search its own database to derive its own new knowledge to respond to learner's questions. Basically, early specifications for ITS or (ICAI) require it to "diagnose errors and tailor remediation based on the diagnosis" (Shute & Psotka, 1994, p. 9).[4] The idea of diagnosis and remediation is still in use today when programming ITS.
A key breakthrough in ITS research was the creation of LISPITS, a program that implemented ITS principles in a practical way and showed promising effects increasing student performance. LISPITS was developed and researched in 1983 as an ITS system for teaching students the LISP programming language (Corbett & Anderson, 1992).[8] LISPITS could identify mistakes and provide constructive feedback to students while they were performing the exercise. The system was found to decrease the time required to complete the exercises while improving student test scores (Corbett & Anderson, 1992).[8] Other ITS systems beginning to develop around this time include TUTOR created by Logica in 1984 as a general instructional tool[9] and PARNASSUS created in Carnegie Mellon University in 1989 for language instruction.[10]
Modern ITSEdit
After the implementation of initial ITS, more researchers created a number of ITS for different students. In the late 20th century, Intelligent Tutoring Tools (ITTs) was developed by the Byzantium project, which involved six universities. The ITTs were general purpose tutoring system builders and many institutions had positive feedbacks while using them. (Kinshuk, 1996)[11] This builder, ITT, would produce an Intelligent Tutoring Applet (ITA) for different subject areas. Different teachers created the ITAs and built up a large inventory of knowledge that was accessible by others through the Internet. Once an ITS was created, teachers could copy it and modify it for future use. This system was efficient and flexible. However, Kinshuk and Patel believed that the ITS was not designed from an educational point of view and was not developed based on the actual needs of students and teachers. (Kinshuk and Patel, 1997)[12]
There were three ITS projects that functioned based on conversational dialogue: AutoTutor, Atlas (Freedman, 1999),[13] and Why2. The idea behind these projects was that since students learn best by constructing knowledge themselves, the programs would begin with leading questions for the students and would give out answers as a last resort. AutoTutor's students focused on answering questions about computer technology, Atlas's students focused on solving quantitative problems, and Why2's students focused on explaining physical systems qualitatively. (Graesser, VanLehn, and others, 2001)[14] Other similar tutoring systems such as Andes (Gertner, Conati, and VanLehn, 1998)[15] tend to provide hints and immediate feedbacks for students when students have trouble answering the questions. They could guess their answers and have correct answers without deep understanding of the concepts. Research was done with a small group of students using Atlas and Andes respectively. The results showed that students using Atlas made significant improvements compared with students who used Andes.[16] However, since the above systems require analysis of students' dialogues, improvement is yet to be made so that more complicated dialogues can be managed.
Intelligent tutoring systems consist of four basic components based on a general consensus amongst researchers (Nwana,1990;[17] Freedman, 2000;[18] Nkambou et al., 2010[19]):
- The Domain model
- The Student model
- The Tutoring model, and
- The User interface model
The domain model (also known as the cognitive model or expert knowledge model) is built on ACT-R theory which tries to take into account all the possible steps required to solve a problem. More specifically, this model "contains the concepts, rules, and problem-solving strategies of the domain to be learned. It can fulfill several roles: as a source of expert knowledge, a standard for evaluating the student's performance or for detecting errors, etc." (Nkambou et al., 2010, p. 4).[19]
The student model can be thought of as an overlay on the domain model. It is considered as the core component of an ITS paying special attention to student's cognitive and affective states and their evolution as the learning process advances. As the student works step-by-step through their problem solving process the system engages in a process called model tracing. Anytime the student model deviates from the domain model the system identifies, or flags, that an error has occurred.
The tutor model accepts information from the domain and student models and makes choices about tutoring strategies and actions. At any point in the problem-solving process the learner may request guidance on what to do next, relative to their current location in the model. In addition, the system recognizes when the learner has deviated from the production rules of the model and provides timely feedback for the learner, resulting in a shorter period of time to reach proficiency with the targeted skills.[20] The tutor model may contain several hundred production rules that can be said to exist in one of two states, learned or unlearned. Every time a student successfully applies a rule to a problem, the system updates a probability estimate that the student has learned the rule. The system continues to drill students on exercises that require effective application of a rule until the probability that the rule has been learned reaches at least 95% probability.[21]
Knowledge tracing tracks the learner's progress from problem to problem and builds a profile of strengths and weaknesses relative to the production rules. The cognitive tutoring system developed by John Anderson at Carnegie Mellon University presents information from knowledge tracing as a skillometer, a visual graph of the learner's success in each of the monitored skills related to solving algebra problems. When a learner requests a hint, or an error is flagged, the knowledge tracing data and the skillometer are updated in real-time.
The user interface component "integrates three types of information that are needed in carrying out a dialogue: knowledge about patterns of interpretation (to understand a speaker) and action (to generate utterances) within dialogues; domain knowledge needed for communicating content; and knowledge needed for communicating intent" (Padayachee, 2002, p. 3).[22]
Nkambou et al. (2010) make mention of Nwana's (1990)[17] review of different architectures underlining a strong link between architecture and paradigm (or philosophy). Nwana (1990) declares, "[I]t is almost a rarity to find two ITSs based on the same architecture [which] results from the experimental nature of the work in the area" (p. 258). He further explains that differing tutoring philosophies emphasize different components of the learning process (i.e., domain, student or tutor). The architectural design of an ITS reflects this emphasis, and this leads to a variety of architectures, none of which, individually, can support all tutoring strategies (Nwana, 1990, as cited in Nkambou et al., 2010). Moreover, ITS projects may vary according to the relative level of intelligence of the components. As an example, a project highlighting intelligence in the domain model may generate solutions to complex and novel problems so that students can always have new problems to work on, but it might only have simple methods for teaching those problems, while a system that concentrates on multiple or novel ways of teaching a particular topic might find a less sophisticated representation of that content sufficient.[18]
Design and development methodsEdit
Apart from the discrepancy amongst ITS architectures each emphasizing different elements, the development of an ITS is much the same as any instructional design process. Corbett et al. (1997) summarized ITS design and development as consisting of four iterative stages: (1) needs assessment, (2) cognitive task analysis, (3) initial tutor implementation and (4) evaluation.[23]
The first stage known as needs assessment, is common to any instructional design process, especially software development. This involves a learner analysis, consultation with subject matter experts and/or the instructor(s). This first step is part of the development of the expert/knowledge and student domain. The goal is to specify learning goals and to outline a general plan for the curriculum; it is imperative not to computerize traditional concepts but develop a new curriculum structure by defining the task in general and understanding learners' possible behaviours dealing with the task and to a lesser degree the tutor's behavior. In doing so, three crucial dimensions need to be dealt with: (1) the probability a student is able to solve problems; (2) the time it takes to reach this performance level and (3) the probability the student will actively use this knowledge in the future. Another important aspect that requires analysis is cost effectiveness of the interface. Moreover, teachers and student entry characteristics such as prior knowledge must be assessed since both groups are going to be system users.[23]
The second stage, cognitive task analysis, is a detailed approach to expert systems programming with the goal of developing a valid computational model of the required problem solving knowledge. Chief methods for developing a domain model include: (1) interviewing domain experts, (2) conducting "think aloud" protocol studies with domain experts,(3) conducting "think aloud" studies with novices and (4) observation of teaching and learning behavior. Although the first method is most commonly used, experts are usually incapable of reporting cognitive components. The "think aloud" methods, in which the experts is asked to report aloud what s/he is thinking when solving typical problems, can avoid this problem.[23] Observation of actual online interactions between tutors and students provides information related to the processes used in problem-solving, which is useful for building dialogue or interactivity into tutoring systems.[24]
The third stage, initial tutor implementation, involves setting up a problem solving environment to enable and support an authentic learning process. This stage is followed by a series of evaluation activities as the final stage which is again similar to any software development project.[23]
The fourth stage, evaluation includes (1) pilot studies to confirm basic usability and educational impact; (2) formative evaluations of the system under development, including (3) parametric studies that examine the effectiveness of system features and finally, (4) summative evaluations of the final tutor's effect: learning rate and asymptotic achievement levels.[23]
Eight principles of ITS design and development
Anderson et al. (1987)[25] outlined eight principles for intelligent tutor design and Corbett et al. (1997)[23] later elaborated on those principles highlighting an all-embracing principle which they believed governed intelligent tutor design, they referred to this principle as:
Principle 0: An intelligent tutor system should enable the student to work to the successful conclusion of problem solving.
- Represent student competence as a production set.
- Communicate the goal structure underlying the problem solving.
- Provide instruction in the problem solving context.
- Promote an abstract understanding of the problem-solving knowledge.
- Minimize working memory load.
- Provide immediate feedback on errors.
- Adjust the grain size of instruction with learning.
- Facilitate successive approximations to the target skill.[23]
Use in practice Edit
All this is a substantial amount of work, even if authoring tools have become available to ease the task.[26] This means that building an ITS is an option only in situations in which they, in spite of their relatively high development costs, still reduce the overall costs through reducing the need for human instructors or sufficiently boosting overall productivity. Such situations occur when large groups need to be tutored simultaneously or many replicated tutoring efforts are needed. Cases in point are technical training situations such as training of military recruits and high school mathematics. One specific type of intelligent tutoring system, the Cognitive Tutor, has been incorporated into mathematics curricula in a substantial number of United States high schools, producing improved student learning outcomes on final exams and standardized tests.[27] Intelligent tutoring systems have been constructed to help students learn geography, circuits, medical diagnosis, computer programming, mathematics, physics, genetics, chemistry, etc. Intelligent Language Tutoring Systems (ILTS), e.g. this[28] one, teach natural language to first or second language learners. ILTS requires specialized natural language processing tools such as large dictionaries and morphological and grammatical analyzers with acceptable coverage.
Applications Edit
During the rapid expansion of the web boom, new computer-aided instruction paradigms, such as e-learning and distributed learning, provided an excellent platform for ITS ideas. Areas that have used ITS include natural language processing, machine learning, planning, multi-agent systems, ontologies, semantic Web, and social and emotional computing. In addition, other technologies such as multimedia, object-oriented systems, modeling, simulation, and statistics have also been connected to or combined with ITS. Historically non-technological areas such as the educational sciences and psychology have also been influenced by the success of ITS.[29]
In recent years, ITS has begun to move away from the search-based to include a range of practical applications.[30] ITS have expanded across many critical and complex cognitive domains, and the results have been far reaching. ITS systems have cemented a place within formal education and these systems have found homes in the sphere of corporate training and organizational learning. ITS offers learners several affordances such as individualized learning, just in time feedback, and flexibility in time and space.
While Intelligent tutoring systems evolved from research in cognitive psychology and artificial intelligence, there are now many applications found in education and in organizations. Intelligent tutoring systems can be found in online environments or in a traditional classroom computer lab, and are used in K-12 classrooms as well as in universities. There are a number of programs that target mathematics but applications can be found in health sciences, language acquisition, and other areas of formalized learning.
Reports of improvement in student comprehension, engagement, attitude, motivation, and academic results have all contributed to the ongoing interest in the investment in and research of theses systems. The personalized nature of the intelligent tutoring systems affords educators the opportunity to create individualized programs. Within education there are a plethora of intelligent tutoring systems, an exhaustive list does not exist but several of the more influential programs are listed below.
Examples in educationEdit
Algebra Tutor PAT (PUMP Algebra Tutor or Practical Algebra Tutor) developed by the Pittsburgh Advanced Cognitive Tutor Center at Carnegie Mellon University, engages students in anchored learning problems and uses modern algebraic tools in order to engage students in problem solving and in sharing of their results. The aim of PAT is to tap into a students' prior knowledge and everyday experiences with mathematics in order to promote growth. The success of PAT is well documented (ex. Miami-Dade County Public Schools Office of Evaluation and Research) from both a statistical (student results) and emotional (student and instructor feedback) perspective.[31]
Mathematics Tutor The Mathematics Tutor (Beal, Beck & Woolf, 1998) helps students solve word problems using fractions, decimals and percentages. The tutor records the success rates while a student is working on problems while providing subsequent, lever-appropriate problems for the student to work on. The subsequent problems that are selected are based on student ability and a desirable time in is estimated in which the student is to solve the problem.[32]
eTeacher eTeacher (Schiaffino et al., 2008) is an intelligent agent that supports personalized e-learning assistance. It builds student profiles while observing student performance in online courses. eTeacher then uses the information from the student's performance to suggest a personalized courses of action designed to assist their learning process.[33]
ZOSMAT ZOSMAT was designed to address all the needs of a real classroom. It follows and guides a student in different stages of their learning process. This is a student-centered ITS does this by recording the progress in a student's learning and the student program changes based on the student's effort. ZOSMAT can be used for either individual learning or in a real classroom environment alongside the guidance of a human tutor.[34]
REALP REALP was designed to help students enhance their reading comprehension by providing reader-specific lexical practice and offering personalized practice with useful, authentic reading materials gathered from the Web. The system automatically build a user model according to student's performance. After reading, the student is given a series of exercises based on the target vocabulary found in reading.[35]
CIRCSlM-Tutor CIRCSIM_Tutor is an intelligent tutoring system that is used with first year medical students at the Illinois Institute of Technology. It uses natural dialogue based, Socratic language to help students learn about regulating blood pressure.[36]
Why2-Atlas Why2-Atlas is an ITS that analyses students explanations of physics principles. The students input their work in paragraph form and the program converts their words into a proof by making assumptions of student beliefs that are based on their explanations. In doing this, misconceptions and incomplete explanations are highlighted. The system then addresses these issues through a dialogue with the student and asks the student to correct their essay. A number of iterations may take place before the process is complete.[37]
SmartTutor The University of Hong Kong (HKU) developed a SmartTutor to support the needs of continuing education students. Personalized learning was identified as a key need within adult education at HKU and SmartTutor aims to fill that need. SmartTutor provides support for students by combining Internet technology, educational research and artificial intelligence.[38]
AutoTutor AutoTutor assists college students in learning about computer hardware, operating systems and the Internet in an introductory computer literacy course by simulating the discourse patterns and pedagogical strategies of a human tutor. AutoTutor attempts to understand learner's input from the keyboard and then formulate dialog moves with feedback, prompts, correction and hints.[39]
ActiveMath ActiveMath is a web-based, adaptive learning environment for mathematics. This system strives for improving long-distance learning, for complementing traditional classroom teaching, and for supporting individual and lifelong learning.[40]
Examples in corporate training and industry Edit
SHERLOCK "SHERLOCK" is used to train Air Force technicians to diagnose problems in the electrical systems of F-15 jets. The ITS creates faulty schematic diagrams of systems for the trainee to locate and diagnose. The ITS provides diagnostic readings allowing the trainee to decide whether the fault lies in the circuit being tested or if it lies elsewhere in the system. Feedback and guidance are provided by the system and help is available if requested.[41]
Cardiac Tutor The Cardiac Tutor's aim is to support advanced cardiac support techniques to medical personnel. The tutor presents cardiac problems and, using a variety of steps, students must select various interventions. Cardiac Tutor provides clues, verbal advice, and feedback in order to personalize and optimize the learning. Each simulation, regardless of whether the students were successfully able to help their patients, results in a detailed report which students then review.[42]
CODES Cooperative Music Prototype Design is a Web-based environment for cooperative music prototyping. It was designed to support users, especially those who are not specialists in music, in creating musical pieces in a prototyping manner. The musical examples (prototypes) can be repeatedly tested, played and modified. One of the main aspects of CODES is interaction and cooperation between the music creators and their partners.[43]
Assessing the effectiveness of ITS programs is problematic. ITS vary greatly in design, implementation, and educational focus. When ITS are used in a classroom, the system is not only used by students, but by teachers as well. This usage can create barriers to effective evaluation for a number of reasons; most notably due to teacher intervention in student learning. Teachers often have the capability to enter new problems into the system, adjusting the curriculum and creating interactions with the students as they learn. All of these variables make evaluation of an ITS complex.[44]
Despite the inherent complexities, numerous studies have attempted to measure the overall effectiveness of ITS, often by comparisons of ITS to human tutors.[45][46][47][48] Reviews of early ITS systems (1995) showed an effect size of d = 1.0 in comparison to no tutoring, where as human tutors were given an effect size of d = 2.0.[45] Kurt VanLehn's much more recent overview (2011) of modern ITS found that there was no statistical difference in effect size between expert one-on-one human tutors and step-based ITS.[48] Some individual ITS have been evaluated more positively than others. Studies of the Algebra Cognitive Tutor found that the ITS students outperformed students taught by a classroom teacher on standardized test problems and real-world problem solving tasks.[49] Subsequent studies found that these results were particularly pronounced in students from special education, non-native English, and low-income backgrounds.[50]
Some recognized strengths of ITS are their ability to provide immediate yes/no feedback, individual task selection, on-demand hints, and support mastery learning.[48][51]
Intelligent tutoring systems are expensive both to develop and implement. The research phase paves the way for the development of systems that are commercially viable. However, the research phase is often expensive; it requires the cooperation and input of subject matter experts, the cooperation and support of individuals across both organizations and organizational levels. Another limitation in the development phase is the conceptualization and the development of software within both budget and time constraints. There are also factors that limit the incorporation of intelligent tutors into the real world, including the long timeframe required for development and the high cost of the creation of the system components. A high portion of that cost is a result of content component building.[19] For instance, surveys revealed that encoding an hour of online instruction time took 300 hours of development time for touring content.[52] Similarly, building the Cognitive Tutor took a ratio of development time to instruction time of at least 200:1 hours.[45] The high cost of development often eclipses replicating the efforts for real world application.[53] Intelligent tutoring systems are not, in general, commercially feasible for real-world applications.[53]
A criticism of Intelligent Tutoring Systems currently in use, is the pedagogy of immediate feedback and hint sequences that are built in to make the system "intelligent". This pedagogy is criticized for its failure to develop deep learning in students. When students are given control over the ability to receive hints, the learning response created is negative. Some students immediately turn to the hints before attempting to solve the problem or complete the task. When it is possible to do so, some students bottom out the hints - receiving as many hints as possible as fast as possible - in order to complete the task faster. If students fail to reflect on the tutoring system's feedback or hints, and instead increase guessing until positive feedback is garnered, the student is, in effect, learning to do the right thing for the wrong reasons. Tutoring systems are unable to detect shallow learning and therefore, the learning for some users is not optimal.[54]
Another criticism of intelligent tutoring systems is the failure of the system to ask questions of the students to explain their actions. If the student is not learning the domain language than it becomes more difficult to gain a deeper understanding, to work collaboratively in groups, and to transfer the domain language to writing. For example, if the student is not "talking science" than it is argued that they are not being immersed in the culture of science, making it difficult to undertake scientific writing or participate in collaborative team efforts. Intelligent tutoring systems have been criticized for being too "instructivist" and removing intrinsic motivation, social learning contexts, and context realism from learning.[55]
Practical concerns, in terms of the inclination of the sponsors/authorities and the users to adapt intelligent tutoring systems, should be taken into account.[53] First, someone must have a willingness to implement the ITS.[53] Additionally an authority must recognize the necessity to integrate an intelligent tutoring software into current curriculum and finally, the sponsor or authority must offer the needed support through the stages of the system development until it is completed and implemented.[53]
Evaluation of an intelligent tutoring system is an important phase; however, it is often difficult, costly, and time consuming.[53] Even though there are various evaluation techniques presented in the literature, there are no guiding principles for the selection of appropriate evaluation method(s) to be used in a particular context.[56][57] Careful inspection should be undertaken to ensure that a complex system does what it claims to do. This assessment may occur during the design and early development of the system to identify problems and to guide modifications (i.e. formative evaluation).[58] In contrast, the evaluation may occur after the completion of the system to support formal claims about the construction, behaviour of, or outcomes associated with a completed system (i.e. summative evaluation).[58] The great challenge introduced by the lack of evaluation standards resulted in neglecting the evaluation stage in several existing ITS'.[56][57][58]
Current and Future directions Edit
Some aspects that human tutors can do that intelligent tutoring systems are still limited by are in the areas of dialogue and feedback. One reason for these limitations are that human tutors are able to interpret the affective state of the student. Current research has been focusing on how to improve on these areas of ITS to make it more effective.
Human tutors have the ability to understand a person's tone and inflection within a dialogue and interpret this to provide continual feedback through an ongoing dialogue. Intelligent tutoring systems are now being developed to attempt to simulate natural conversations. To get the full experience of dialogue there are many different areas in which a computer must be programmed; including being able to understand tone, inflection, body language, and facial expression and then to respond to these. Dialogue in an ITS can be used to ask specific questions to help guide students and elicit information while allowing students to construct their own knowledge.[59] The development of more sophisticated dialogue within an ITS has been a focus in some current research partially to address the limitations and create a more constructivist approach to ITS.[60]
There is a growing body of work that is considering the role of affect on learning and trying to develop intelligent tutoring systems that will be able to interpret and adapt to the different emotional states.[61][62] Humans do not just use cognitive processes in learning but the affective processes they go through also plays an important role. For example, learners learn better when they have a certain level of disequilibrium (frustration), but not enough to make the learner feel completely overwhelmed.[61] This has motivated affective computing to begin to produce and research creating intelligent tutoring systems that can interpret the affective process of an individual.[61] An ITS can be developed to read an individual's expressions and other signs of affect in an attempt to find and tutor to the optimal affective state for learning. There are many complications in doing this since affect is not expressed in just one way but in multiple ways so that for an ITS to be effective in interpreting affective states it may require a multimodal approach (tone, facial expression, etc...).[61] These ideas have created a new field within ITS, that of Affective Tutoring Systems (ATS).[62] One example of an ITS that addresses affect is Gaze Tutor which was developed to track students eye movements and determine whether they are bored or distracted and then the system attempts to reengage the student.[63]
ITS conference Edit
The Intelligent Tutoring Systems conference is typically held every other year. It was hosted in Montreal (Canada) by Claude Frasson and Gilles Gauthier, in 1988, 1992, 1996 and 2000; in San Antonio (US) by Carol Redfield and Valerie Shute in 1998; in Biarritz (France) and San Sebastian (Spain) by Guy Gouardères and Stefano Cerri in 2002; in Maceio (Brazil) by Rosa Maria Vicari and Fábio Paraguaçu in 2004; in Jhongli (Taiwan) by Tak-Wai Chan in 2006. The conference was recently back in Montreal in 2008 (for its 20th anniversary) and hosted by Roger Nkambou and Susanne Lajoie. ITS 2010 was held in Pittsburgh (US), hosted by Jack Mostow, Judy Kay, and Vincent Aleven. The 2012 conference was held in Chania (Crete). The International Artificial Intelligence in Education (AIED) Society publishes The International Journal of Artificial Intelligence in Education (IJAIED) and produces the International Conference on Artificial Intelligence in Education every odd numbered year. The American Association of Artificial Intelligence (AAAI) will sometimes have symposia and papers related to intelligent tutoring systems. A number of books have been written on ITS including three published by Lawrence Erlbaum Associates.
See alsoEdit
- Educational technology
- Instructional technology
- Learning objects
- Serious games
- Educational data mining
Key textsEdit
- (2010) Advances in Intelligent Tutoring Systems, Springer.
- Woolf, Beverly Park (2009). Building Intelligent Interactive Tutors, Morgan Kaufmann.
- (2005) One-on-one Tutoring by Humans and Computers, Routledge.
- (1988) Foundations of Intelligent Tutoring Systems, Lawrence Erlbaum.
- (1988) Intelligent Tutoring Systems: Lessons Learned, Lawrence Erlbaum.
- Wenger, Etienne (1987). Artificial Intelligence and Tutoring Systems: Computational and Cognitive Approaches to the Communication of Knowledge, Morgan Kaufmann.
- (1983) Computer-Assisted Instruction: Its Use in the Classroom, Prentice-Hall Inc..
- (1982) Intelligent Tutoring Systems, Academic Press.
- Intelligent Tutoring Systems: An Historic Review in the Context of the Development of Artificial Intelligence and Educational Psychology
- Intelligent Tutoring Systems: The What and the How
- (2000). What is an Intelligent Tutoring System?. Intelligence 11 (3): 15–16.
- Intelligent Tutoring Systems: Using AI to Improve Training Performance and ROI
- A Framework for Model-Based Adaptive Training
- A Conceptual Framework for Internet based Intelligent Tutoring Systems
- Intelligent Tutoring Systems with Converersational Dialogue
Additional materialEdit
External linksEdit
- Intelligent Tutoring Systems in the Encyclopedia of Educational Technology
- Intelligent Tutoring Systems in the AI topics website of the [http://www.aaai.org/ American
Association for Artificial Intelligence]
- Intelligent Tutoring Systems: An Historic Review in the Context of the Development of Artificial Intelligence and Educational Psychology
- Intelligent Tutoring Systems: The What and the How
- (2000). What is an Intelligent Tutoring System?. Intelligence 11 (3): 15–16.
- Intelligent Tutoring Systems: Using AI to Improve Training Performance and ROI
- A Framework for Model-Based Adaptive Training
- A Conceptual Framework for Internet based Intelligent Tutoring Systems
- Intelligent Tutoring Systems with Converersational Dialogue
|This page uses Creative Commons Licensed content from Wikipedia (view authors).
Cite error:
<ref> tags exist, but no
<references/> tag was found


text:
On the Grill Right Now!
From food to music to sports, MoMS exhibitions cover our nation's history.
Where do you want to find a MoMS exhibiiton in the U.S.?
Key Ingredients lesson plans, reading lists, etc., for teachers and educators!
What's going on MoMSland today?
Key Ingredients news on Twitter:

subset: commoncrawl


text:
Please select from the buttons below, whether you wish to submit either a Business or Personal finance application.
* All images are for illustration purposes only, errors and omissions are excluded and the deal could end without notice. Smart Leasing Solutions is an appointed representative of AutomotiveCompliance who is authorised and regulated by the Financial Conduct Authority FCA No497010and are a credit broker and not a lender. Vehicle sourcing fee applies.
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Smart Leasing Solutions Limited is an Appointed Representative of Automotive Compliance Ltd, which is authorised and regulated by the Financial Conduct Authority (FCA No 497010). Automotive Compliance Ltd's permissions as a Principal Firm allows Smart Leasing Solutions Limited to act as a credit broker, not as a lender, for the introduction to a limited number of finance providers only.
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Riedel – What's in Your Cabinet?
Each year, Hospice du Rhône produces an exciting three-day wine event that is regarded as the single most educational and enjoyable gathering of international Rhône wine producers and enthusiasts. Attendees are treated to four in-depth wine seminars, a well as wine tastings and some great meals. But, arguably the most valuable aspect of this fabulous event is the opportunity for attendees to rub elbows with the very producers and winemakers whose enthusiasm for Syrah, Grenache and twenty other varieties of Rhône Valley grapes has inspired the passion in all of us.
We return to the 2009 HdR with our coverage of a somewhat unusual Riedel seminar. Normal Riedel seminars cover how different shapes of stemware affect the aroma and taste of different grape varieties. However, this seminar was devised to illustrate the differences of three syrah-based wines (2005 E. Guigal – St. Joseph, 2005 Elderton Shiraz – Barossa, 2007 Miner/Villard "La Diligence" Stagecoach Vnyd, Napa) when sampled from 4 different stems. Even more interesting, 3 of the 4 glasses were designed specifically for Syrah-based wines.
The "science" behind the shape of the wine glass seems to always cause conversation. Most non-science/engineering types of people are unable to explain the phenomena. Riedel explains it this way: the shape of the glass dictates the emission of aroma, somewhat based on how a person positions their head while drinking, which ultimately alters the way the wine flows into the mouth. By narrowing the rim of the glass, this focuses the drinker to tilt his head back, delivering the wine to potentially different zones of the tongue, which results in the brain perceiving different flavors. The volume of the glass, the diameter of its rim, the thickness of the crystal, and the finish also all play a role in the roll of the wine onto the tongue.
The Stemware used for this event were: Riedel Restaurant = Oregon Pinot glass #0447/07; Sommeliers = Hermitage 0400/30; Vinum Extreme = Syrah 4444/30; and Flow = Syrah 0407/30. that seeks to find the differences Based on the tried – and arguably true – impression that wines taste and smell different when consumed from different stemware.
For more info on Hospice du Rhone: www.hospicedurhone.org
For more info on Riedel, Wine Glass Company: www.riedel.com
Sponsor: VinAssure, Wine Preservation System: www.vinassure.com
Click Below to Play the Show:
(1:16:36min 55MB)
Podcast: Play in new window | Download


text:
Responsify lets web designers & developers quickly and effortlessly generate a responsive grid framework and customise it as they wish, all through an easy to use interface. It is ideal for creating a solid, standard's compliant foundation, from which they can build upon.
This was designed & developed as my Major Project during my Final year at Uni and is one of my favourite projects to date.View the Site
Garvan Traynor Jewellery is an award winning Belfast based business established in 2005. The business specialises in private commissions, designing one-off pieces of contemporary jewellery using a variety of metals and precious stones, including platinum and diamonds.
My main focus for this project was to create a simple, elegant, responsive site to showcase the beautifully crafted jewellery. I was also responsible for creating a logo for the business, which again had to be simple, yet striking.View the Site
The Belfast Mela was created in 2007, by ArtsEkta, Northern Ireland's leading ethnic arts organisation. It has since become established as an unmissable highlight in Belfast's festival line-up and has rapidly reached status as Northern Ireland's largest multi-cultural festival, attracting a collective number of 60,000 visitors over four years.
I designed and built the Mela website while on placement with the Belfast based web design studio Fresh Made Media.View the Site
Performance Hardware are a PC repair company based in Ballymena. They offer a wide range of services to both domestic and business customers, whether it's an upgrade, a repair or an installation. They've got it covered!
The focus for the site was to inform potential & existing customers of the wide range of services offered by Performance Hardware, as many customers weren't aware of these.View the Site
I designed and coded this store as part of a University module. I used the Performance Hardware brand and integrated the store into the existing site I had created. This project allowed me to improve my PHP and Javascript (jQuery) skills.
The store functions like an online application, in terms of it's responsiveness in loading content, this was achieved with AJAX. Although it's a little rough around the edges (no URL rewriting), it's only a prototype and I am very pleased with the result.View the Site
More samples of my work available on request.
I am happiest when I am designing, whether it's planning a layout on paper or sitting in front of a screen moving pixels. Last year I graduated with a first-class honours in Interactive Multimedia Design and while I enjoyed my time at University I am glad to be freelancing fulltime, working together with clients to solve their problems and help them grow their businesses.
When I'm not nudging pixels around on a screen, you may find me playing video games online, reading books on my Kindle, or watching a healthy (insane) amount of TV shows.
This site is based on a 960px grid and built using the latest HTML & CSS. The typefaces I'm using are the lovely Open Sans for the headings and Proxima Nova for the body copy, both served through Typekit.
This site is responsive, which means the content will scale to fit on a variety of devices, like your mobile phone or tablet. Resize your browser window to see it in action!
If you like what you've seen, get in touch.


text:
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text:
Cypress, CA - June 19, 2012 - Yamaha Motor Corporation, U.S.A., is pleased to announce another incredible opportunity to bid on a historic set of road racing leathers as worn by Monster Energy/Tech 3 MotoGP star Cal Crutchlow, during his dramatic sixth-place finish at last weekend's British Grand Prix, all to benefit Feed the Children, Yamaha Motor Corporation's corporate charity.
The leathers being auctioned are directly off the back of "The Honey Badger" himself, Monster Energy/Tech 3 Yamaha's Cal Crutchlow, who has generously donated his authentic Spidi custom-made leathers, as worn by Cal during this year's British Grand Prix.
The winning bidder will also receive a pair of weekend 3-day general admission passes, including paddock access, to the Red Bull U.S. Grand Prix, July 27-29 at Mazda Raceway Laguna Seca. In addition, the package includes hotel accommodations in the Monterey area for the entire race weekend (Friday and Saturday night).
And, finally, the winning bidder will also be invited on stage Saturday evening during the scheduled, GE-sponsored concert ceremonies to receive the personally autographed gear directly from Cal.
"Cal Crutchlow has quickly proven himself to be tremendously popular with race fans the world over, and there's no doubt that he has gained legions of new fans after his heroic charge through the pack on his #35 Monster Energy/Tech 3 Yamaha YZR-M1 this past weekend," said Bob Starr, Yamaha's General Manager of National Communications, "And, to donate his actual race-worn leathers to such a worthy cause and see him on the GE stage at this years' Red Bull U.S. GP will surely be a treat for not only all his American fans but, in particular, the winning bidder!"
The auction is live on eBay beginning today and will continue through June 26. To place your bid, please visit the following link:


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Saw you guys out on the water yesterday. Tried searching for "ICU" or "I See You" on the site, but nothing came up. Anyone know this Freedom 25 sailing in Buzzards Bay, MA? Would love to get in contact so as to know some fellow "smaller" freedom owners in the area.
Best wishes
Jesse (F21 owner)


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Yes, you can record 4K videos via Mi A2. It does support 4K video recording.
No. Mi A2 has USB Type C port and it also comes with USB Type C to 3.5 mm headphone adapter in the box. So, there will not be any problem to use Mi A2 with your 3.5 mm headphones.
Yes, Mi A2 does have the face unlock functionality.
Yes, Mi A2 is made of Aluminium Alloy.
Mi A2 does support the fast charging. It takes about 90 minutes to fully charge the phone.
I am using the phone since last 15 days and I absolutely love this phone. Excellent phone. A must buy.
The Mi A2 continues Xiaomi's now-familiar tradition of offering excellent value. The upgrades from last year make it an enticing option in this category, and the camera, in particular, is stunning.
The Xiaomi Mi A1 was an incredible value proposition in its market segment, the Mi A2 falls somewhat short. It takes away key features like storage expansion and universal headphone connectivity, plus it misses the mark in crucial areas like display and battery life. Affordable as it may be, it wouldn't be our default choice in the midrange quite like the A1 was.


text:
Over the last 24 hours, since the release of Ubuntu 11.04, I've managed to upload over 200GB on the torrents. Here's a little graph of my bandwidth use. I'm sure this is just a drop int he bucket. It'd be interesting to see how much other individuals upload during the same release time.
I don't do as much as I should to work with the community, but at least I share!


text:
The locale setting defines the language of your user interface and the display formats for information like time, date, and currency. MATLAB® uses the user-specified locale on all platforms.
If MATLAB does not correctly display characters in your language, you might have a locale setting problem. Locale is composed of individual settings which you can control. Each platform uses different parameters to specify the locale setting. The following terms are relevant to understanding locale settings.
locale — Format:
For example, for the U.S. English locale setting
en means that the
display language is English.
US indicates that time and date
displays use U.S. conventions.
US-ASCII is the coded
character set (codeset) used to display text.
character set — Set of characters that make up a language used by a region. The MATLAB supported character set is the character set specified by the user locale setting.
codeset — Abbreviation for coded character set. A set of characters with a unique numerical value assigned to each character.
encoding — Scheme for assigning numerical values to a character set to create a codeset.
7-bit ASCII — Either the codeset or the characters contained in that codeset. There are 128 characters, which include letters, digits, symbols, control characters, and graphics characters. The term ASCII in MathWorks® documentation is the same as 7-bit ASCII.
Unicode — Character codeset. Excerpt from the unicode.org website: "Unicode® provides a unique number for every character, no matter what the platform, no matter what the program, no matter what the language."
user locale setting — Setting on your computer
that specifies the locale that you want to use when running MATLAB. If your user-specified locale is not supported, MATLAB uses the default locale
system locale setting — Setting on Microsoft® Windows® platforms. The user locale and system locale must be the same value. If these values are not the same, you might see garbled text or incorrectly displayed characters.
i18n — Short for the word
internationalization, where
18 stands
for the number of letters between the letters
i and
MATLAB does not support every locale setting. If the user-specified locale is
unsupported, MATLAB uses the default locale
en_US_POSIX.US-ASCII, also
known as C locale.
MATLAB supports the character set specified by the user locale setting.
In the Current Folder Browser, MATLAB usually uses platform-neutral localized formats and rules. You can, however, use the operating system short date format to control the format for displaying file date and time data.


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text:
Volcanic Theory of Water
Clip: Season 37 Episode 11 | 1m 58s
The origins of water on earth have long been debated. Recent evidence suggests that water was always here, trapped beneath the crust. Volcanoes acted like a tear in the surface of our planet allowing the water to slowly escape as vapor. This condensed in our atmosphere and fell as rain, which eventually turned the planet from barren to blue.
Aired: 02/20/19Video has closed captioning.
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Major support for NATURE is provided by The Arnhold Family in memory of Henry and Clarisse Arnhold, Sue and Edgar Wachenheim III, The Fairweather Foundation, the Kate W. Cassidy Foundation, Kathy Chiao and Ken Hao, the Lillian Goldman Charitable Trust, Charles Rosenblum, the Filomen M. D'Agostino Foundation, Rosalind P. Walter, Sandra Atlas Bass, Doris R. and Robert J. Thomas, The Hite Foundation, Gary and Christy Roeber, The Sun Hill Family Foundation in memory of Susan and Edwin Malloy, The Arlene and Milton D. Berkman Philanthropic Fund, the Bradley L. Goldberg Family Foundation, the Anderson Family Fund, The M. & H. Sommer Foundation, Ron Hull, by the Corporation for Public Broadcasting, by PBS, and by Viewers Like You.


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A generic internal rotary pump. (courtesy pumpschool.com)
Internal rotary pumps are used for practically all capacities and pressures. Internal rotary pumps can be operated at varying speeds and in reverse which provides a large amount of flexibility. The mechanical efficiency of internal rotary pumps when handling liquids with good lubricating value is good. Overall efficiency increases with flow rate and discharge pressure to a maximum of around 75%.
Read Full Article


text:
Gosh, what's a bigger story -- that to the extent it was ever happening at all the housing recovery "seems to have been aborted," or that according to the government there was very little inflation in October?
Readers probably didn't hear much about the Census Bureau's terrible reports Wednesday about what happened in the U.S. housing market in October. There's a reason for that, which is that the Associated Press, on which the nation over-relies for business news, paid very little heed to the Bureau's reports, and in fact co-opted their awfulness to pat Ben Bernanke on the back for his quantitative easing efforts.
The AP might counter with the excuse that the news about inflation was (supposedly) more important. The Bureau of Labor Statistics reported today that prices rose 0.2% in October, and were flat except for food and energy. Even if we concede its relative significance (which I don't), are we supposed to believe that the Essential Global News Network's business reporters can't provide detailed coverage of more than one big story at a time?
There is no story specifically dedicated to the housing news at the AP's main site, as illustrated in the graphic that follows. It shows the results of a search on "housing," and lists all stories that have appeared or have been updated since 8:30 this morning, when the Census Bureau issued its report about housing starts and building permits:
Here is what Marty Crutsinger and Chris Rugaber had to say about inflation and the housing news in the "Tame inflation gives Fed ammo for bond-buying plan" listed above:
Consumer prices barely changed for the third straight month, strengthening the Federal Reserve's hand at a time when it is defending a plan to boost the economy by buying more government debt.
Extraordinarily low inflation was a major impetus for the Fed program to spend $600 billion buying Treasury bonds. A report Wednesday from the Labor Department showed that inflation remains super-low.
A steep rise in gasoline prices drove the consumer price index up 0.2 percent in October, the fourth straight monthly increase. But excluding volatile food and energy costs, core consumer prices were unchanged for the third straight month.
... the Commerce Department said construction of new homes and apartments sank 11.7 percent last month to a seasonally adjusted annual rate of 519,000 units. That was mainly because apartment construction, which represents less than 20 percent of the housing market, fell by more than 40 percent. The much larger single-family home category fell 1.1 percent.
Still, the drop reduced the pace of home construction to its weakest point since April 2009, when it reached the lowest level on records dating to 1959.
... "The data is definitely in the Fed's camp today and should help keep the Fed's critics at bay," said Christopher Rupkey, chief financial economist at Bank of Tokyo-Mitsubish. "There is nothing in this data that would push the Fed off its course of continuing to buy government securities. Inflation is getting closer to becoming deflation and the recovery in housing seems to have been aborted."
A troublesome sign in the October report were price declines on new cars and clothing.
While flat prices may seem like a good thing for shoppers, the Fed would like to see prices rise more quickly to keep deflation at bay.
Crutsinger and Rugaber really wrote that inflation isn't just low, it's "super-low." I guess if next month's CPI adjustment is near zero, it will be "super-duper low." Zheesh.
As to housing, here is how bad it really is, using not seasonally adjusted, or NSA, numbers (i.e., what really happened):
- The number of NSA housing starts (long PDF here) in October 2010 (44,300) was the lowest on record for any October since records have been kept. October 2009, a terrible month, was barely higher (44,500). October 2008 (68,200) was the third-worst. No other October going back to 1959 came in below 75,000, and the vast majority have been well over 100,000.
- The NSA number of single-family housing starts in October 2010 (36,400) was down 7.6% from October 2009 (39,400), and is another all-time low since records have been kept.
- Meanwhile, actual starts for dwellings of 5 units or more jumped over 50% from October of last year (4,700) to this year (7,100). These are still historically poor numbers, but even worse they may be an indication that there may be a possibly permanent move towards multi-unit dwellings and away from single-family units.
- The news in building permits is just as bad (long PDF here). The October 2010 NSA total (43,700) came in almost 9% lower than October 2009 (47,900); the single-family decline was worse (from 38,600 to 31,900, or -17%). Both numbers are all-time October lows since records have been kept. The mix of permits also leaned more towards 5-plus unit dwellings.
Actual housing starts on the whole have trailed last year's awful levels in four of the past five months; as far as single-family homes are concerned, the current losing streak compared to last year is five months in a row. The re-collapse in housing is far more significant news than the fact that inflation came in as expected.
Cross-posted at BizzyBlog.com.


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text:
Polymorphism makes you use truly unique identifiers. If you don't agree, consider the following scenario:
Imagine, that client wants you to create a registry (list view, whatever), that will enable him to query assets and apply different criteria onto them (there is not much in the interface, but name can also be queried with SQL like operator). You immediately notice, that you can use polymorphic queries from NHibernate, which spans to all implementations of the given interface. They can be some problems with the queries efficiency (paging is done in the memory), but skipping this fact, you've done it. You have your list of assets presented to the user, who instantly notices that there are a few rows with the same id. What's going on, you ask. Ahhh, you're still using the old-fancy db identity identifier generator. Not only it's generates unique id per table, but also removes all the bonuses connected with batching inserts. How can you overcome such an obstacle?
The solution is simple: use GuidGenerator, especially CombGuidGenerator, as your IIdentityGenerator. If your customer hastes these 128-bit long chains, use HiLo algorithm, which stores within the process a package of identifiers ready to be used. Once they're used, it goes to the db to transactionally get another one. Span this HiLo for all tables/entities hierarchies which can be queried in a polymorphic way. For the best result, set it for all entities' types, and enjoy having unique long/int identifiers across your whole database.
Polymorphism and identifiers
Polymorphism makes you use truly unique identifiers. If you don't agree, consider the following scenario:


text:
We have a special guest post by Vincent Zandri, author of the thriller novel, Moonlight Falls, today.
Moonlight Falls is the Albany, New York-based paranoid tale (in the Hitchcock tradition) of former APD Detective turned Private Investigator/Massage Therapist, Richard "Dick" Moonlight, who believes he might be responsible for the brutal slaying by knife of his illicit lover, the beautiful Scarlet Montana. The situation is made all the worse since Scarlet is the wife of Moonlight's boss, Chief of Detectives Jake Montana.
Why does Moonlight believe he might be responsible?
He's got a small fragment of a .22 hollow point round buried inside his brain, lodge directly up against his cerebral cortex. The result of a botched suicide attempt four years prior to the novel's start, an operation to remove the bullt frag would be too dangerous.
But the bullet causes Moonlight lots of problems, the least of which are the occasional memory loss and his rational ability to tell right from wrong. The bullet frag also might shift at any moment, making coma and/or sudden death, a very real possibility.
Still, Moonlight has been trying to get his life together as of late.
But when Scarlet begs him to make the trip over to her house late one rainy Sunday night to issue one of his "massages," he makes a big mistake by sleeping with her. Later, having passed out in her bed, he will be rudely awakened by a garage door opening and Jake's unexpected and very drunken homecoming. Making his impromptu escape out a top floor window, Moonlight will seek the safety of his home.
Two hours later however, he will receive another unexpected visit from Jake Montana. This time the big Captain has sobering news to report. He's discovered his wife's mutilated body in her own bed. She's been murdered and now he needs the P.I. to investigate it in association with Albany 's "overtaxed" Special Independent Unit before I.A. pokes their nose into the affair. Moonlight takes a big step back. Is it possible he made a second trip to the Montana home-sweet-home and just has no recollection of it? Once there, did he perform a heinous crime on his part-time lover? Or is this some kind of set up by his former boss? Is it really Jake who is responsible for Scarlet's death? Does he wish for Moonlight to cover up his involvement, seal the case before Internal Affairs starts poking their nose into the situation?
There's another problem too.
Covering Moonlight's palms and the pads of his fingers are numerous scratches and cuts. Are these defensive wounds? Wounds he received when Scarlet put up a struggle? Or are they offensive wounds? Wounds he couldn't avoid when making his attack on Scarlet with a blade? The answer is not so simple since Moonlight has no idea where he acquired the wounds.
Having no choice but to take on the mission (if only to cover his own ass), Moonlight can only hope the answers to his many questions point to his former boss and not himself.
This is the exciting premise and here are five things Vincent Zandri learned while writing the novel. Enjoy!
Five Things I Learned While Writing Moonlight Falls
by Vincent Zandri
Many things were learned during the five on-again, off-again years while I was writing my new noir, thriller, Moonlight Falls, the least if which, is that initial publishing success can be fading. Back in 1999, when my first commercial thriller, As Catch Can, was first published in hardcover by Delacorte, I assumed that I had found a permanent literary home for the rest of my life, and that the next stop in my green career was the Pulitzer Prize. But when Delacorte merged with another publisher, many of its authors were quickly transferred elsewhere and from there, kindly shown the door. For me, it was back to square one.
But despite the trial and tribulations of a commercial publishing world that has been described as "perilous," I was nonetheless able to adhere to a program of good, solid writing, day in and day out. That alone became my shield against a volatile publishing business. That alone was my guiding force in a short literary life that had seen great ups and that now, was realizing a very deep, seemingly bottomless pit.
Still I trudged on through a period of several years where I did not publish a single book, but instead concentrated on the writing of several manuscripts, not the least of which, became Moonlight Falls. Here are five things I learned about myself and the world around me during that time.
1. Nothing replaces rock solid writing, research and rewriting. Or, the three R's, if you will. Even though I might have quit the business altogether and moved on to something less volatile than the writing and publishing life, I still adhered to a rigorous writing program day in and day out, even when there was no money coming in. I chose this path because in the back of my head, I always knew that the novel would one day be published. Not self-published, mind you, but published in the traditional format. Which leads me to…
2. Never lose your faith in yourself and your ability, even in the face of domestic non-tranquility. Things around the house during my, lets call them "wilderness years," were not very happy. I'd just married my second wife, Laura, whom I believed was my soul mate. We came together at a time when things were great. I was on top of the world as a writer and we were traveling the world. But then, when things got hard. I retreated back into my shell and nearly lost all confidence in my ability to write a great story. But curiously, and sadly, as Laura and I began to break up, I regained my confidence. Which leads me to…
3. Don't quite the day job. Or in my case, don't give up the freelance writing and journalism because you're suddenly under the impression you're the next Norman Mailer. What you must constantly remind yourself is that even a world renowned writer like Mailer was broke half the time. When I published As Catch Can and the follow up, Godchild, I assumed I'd never have to write another stitch of journalism again; that I could place all my literary eggs into one basket. Turns out, had I kept my foot in the freelance writing door, I might have saved my marriage and my home by maintaining at least a semblance of income. Luckily, I was able to make a return to journalism but only after the domestic damage was done. Which leads me to…
4. Learn to weather the storms and know when to move on with your life. Said another way, learn how to swallow you pride. It's a tough thing losing everything you have worked so hard for in life, from your publisher to your wife to your home. But to have it all happen at once, well, that's enough to break even the strongest man for good. But this is the life we live as writers and novelists. This is the life we have chosen. While in many ways I would stop at nothing to have my wife back, I know I am powerless to do anything about it other than write the best I can, and do so consistently and without prejudice; without concern for what the publishing market is currently bearing. Which leads me to…
5. The publishing market is undergoing severe and rapid change. Traditional commercial publishers are dying. Don't let them tell you otherwise. What's replacing them are electronically based, independent houses that although utilizing the traditional publishing model of accepting a manuscript based upon its merits as a work of art, now publish the manuscripts in both electronic and POD format. Yes, the independent bookstores will hate you for it, and even turn up their noses at you. But 90% of all book buyers are making their purchases online. Many of them are doing so via Kindle, I-Phone, BlackBerry, and other electronic means. It's the new world publishing model of social media, virtual tours, book trailers, blog talk radio, mommy blogs, etc., and it is here to stay. More then likely, it will give over to an influx of self-published material over the next few years, while big agent firms and big publishers die off.
Moonlight Falls author, Vincent Zandri, is an award-winning novelist, essayist and freelance photojournalist. His novel As Catch Can (Delacorte) was touted in two pre-publication articles by Publishers Weekly and was called "Brilliant" upon its publication by The New York Post. The Boston Herald attributed it as "The most arresting first crime novel to break into print this season." Other novels include Godchild (Bantam/Dell) and Permanence (NPI). Translated into several languages including Japanese and the Dutch, Zandri's novels have also been sought out by numerous major movie producers, including Heyday Productions and DreamWorks. Presently he is the author of the blogs, Dangerous Dispatches and Embedded in Africa for Russia Today TV (RT). He also writes for other global publications, including Culture 11, Globalia and Globalspec. Zandri's nonfiction has appeared in New York Newsday, Hudson Valley Magazine, Game and Fish Magazine and others, while his essays and short fiction have been featured in many journals including Fugue, Maryland Review and Orange Coast Magazine. He holds an M.F.A. in Writing from Vermont College and is a 2010 International Thriller Writer's Awards panel judge. Zandri currently divides his time between New York and Europe. He is the drummer for the Albany-based punk band to Blisterz.