text: International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 05 Issue: 09 | Sep 2018 p-ISSN: 2395-0072 www.irjet.net Using Data Mining to Predict Students Performance Madhumitha S1, Vaishali Roy2, Vinitha B3,Vijayakumar S4 1,2,3Department of Computer Science and Engineering, R.M.K. Engineering College, Tamil Nadu, India 4Associate Professor, Department of Computer Science and Engineering, R.M.K. Engineering College, Tamil Nadu, India Abstract - Educational data mining is used to extract student data from dataset and transform it into a meaningful and useful structure for further use. In this project, we are using Logistic Regression algorithm to predict student's performance. The existing system uses decision tree, Naive Bayes algorithms to classify the student data. As the decision tree algorithm makes use of numerical values to analyse the data, so it is inefficient in prediction. On the other hand, though the Naive Bayes approach uses textual attributes there exists dependencies among the variables. Hence these algorithms are comparatively inaccurate. The proposed system uses Logistic Regression which is a statistical method for analysing a dataset in which there are one or more independent variables that determines an outcome. Individual attributes are also analysed that influences students' final performance. Key Words: Educational Data Mining, dataset, Predict student performance, Logistic Regression. 1. INTRODUCTION Analyzing the huge amount of data to form useful information is a tedious task for human. Data Mining is the area which analyses large volume of data to extract necessary or useful information. Computers can process data like texts, images, and numbers. This performs analysis based on patterns, association, relations in these data to get useful information. The prediction in students' performance is beneficial as it helps in identifying students with low academic performance at the early stage of academics. In universities, high students' performance results in job opportunities for students and helps college with increased admission rate for the next academic year. The steps to assist the low academic students with better education are: (a) Generation of data source which consists of predictive variables. (b) Identification of various factors which affects the performance of student's learning. (c) Construction of a prediction model with the help of data mining technique on the basis of predictive variables which were identified. (d) Validation of the model. Data Mining can assist in the field of education to extend our understanding of learning process by identifying variables and evaluating them. Mining in the field of education is known as Educational Data Mining. Students' attendance in college, hours spent on a daily basis after college, grades, study hours, family income of the students are significantly related to student performance. By means of Logistic Regression model, it has been found that the factors like attendance and study hours are highly correlated with the performance of the student. This helps us to, increase students' academic achievements. Students' performance prediction helps to identify the most effective factors which work with student's test score and then tune these factors to make better student test performance. 2. RELATED WORK J K Jothi and K Venkatalakshmi conducted the students' performance analysis on the students' data collected from the Villupuram college of Engineering and Technology. The data included five year period and applied clustering methods on the data to overcome the problem of low score of students, and to increase students' academic performance [1]. Sheik and Gadage have done the analysis related to the student learning behavior by using different data mining models, namely classification, clustering, decision tree, pattern mining and text mining [2]. Mythili M S and Shanavas A R applied classification algorithms to analyze and evaluate school students' performance using weka. They came with various classification algorithms, namely J48, Random Forest, Multilayer perception, IBI and decision tree with data collected from the student management system [3]. Suyal and Mohod applied the association and classification rule to identify the students' performance. They mainly focused to find students who need special attention to reduce failure rate [4]. © 2019, IRJET | Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 6747 International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 05 Issue: 09 | Sep 2018 p-ISSN: 2395-0072 www.irjet.net Noah, Barida and Egerton conducted a study to evaluate students' performance by grouping the grading into various classes using CGPA. They used different methods like Neural network, Regression and K-means to identify the weak performers for improving performance [5]. Backer and Yacef conducted a study for identifying the best model for EDM. They analyzed data and reached the conclusion that most of the papers includes prediction than relationship mining [6]. Angeline D M conducted a study on the students' performance by using Apriori algorithm that extracts set of rules specific to each category and analyze the knowledge to classify the student based on their involvement in assignment, internal assessment test, group action etc. It helps to identify the students' range like average, below average, and good performance [7]. Remesh, Parkavi, and Yasodha conducted a study on the placement chance prediction by applying different techniques such as Naive Bayes Simple, MultiLayerPerception, SMO, J48, and REPTree by its accuracy. From the result they concluded that MultiLayerPerception is more suitable than other algorithms [8]. Bhise, Thorat and Supekar suggested a method using K-means clustering algorithm by describing it step by step. This research focused on reducing drop-out-ratio of the students and improve it by considering the factors like midterm and final exam assignment. They considered clustering techniques namely hierarchical, partitions, and categorical [9]. ElGamal A F presented a study for predicting students performance in a programming related course. Here the data is collected from the department of computer science from Mansoura University and applied extract rules for predicting students' performance in programming related course [10]. 3. METHODOLOGY Data mining is the knowledge discovery process from huge volume of data. The mechanism works using data set where the student performance in the end semester is evaluated. 3.1 Data preparation Student related data were collected from a large data set. In this step, data is checked for null values, errors and corrected if any. 3.2 Data selection and transformation In this step only fields which were necessary for the data mining process were selected. The student's 10th, 12th, degree marks in each semester, assignment, study hours, parent's education, income were taken as attribute values for prediction. 3.3 Logistic Regression Algorithm Logistic regression is a classification method where the dependent variable can have only two possible values. When compared with linear regression, Logistic Regression is preferred because data plotting shows that it does not suit linear regression pattern. The logistic regression equation will be Π(x) = 1/1+ -y (1) Where y = β0 + β1X1 + β2X2 + β3X3 + β4X4 and e being Euler's number β0 is the constant β1 is coefficient for variable X1 β2 coefficient for X2 © 2019, IRJET | Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 6748 International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 05 Issue: 09 | Sep 2018 p-ISSN: 2395-0072 www.irjet.net β3 coefficient for variable β4 coefficient for variable Table -1: Student Related Variables 4. EXPERIMENTAL RESULTS After testing with selected attributes using Logistic Regression, the accuracy increased to 82.03%, which is the best among all the models we tested so far. By observation from the bar chart it is evident that around 230 students will perform poor, 600 students will fairly perform and 200 students will perform well in their semester examinations. This helps the university to predict the students' performance in a better manner and give appropriate training to improve the academic results. Table -2: Model Performance © 2019, IRJET | Model Accuracy Naive Bayes 80.76% K-Nearest Neighbor (K=4) Logistic Regression 76.96% Multi-Layer Perceptron J4.8 78.73% Impact Factor value: 7.211 82.03% 81.77% | ISO 9001:2008 Certified Journal | Page 6749 International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 05 Issue: 09 | Sep 2018 p-ISSN: 2395-0072 www.irjet.net Fig -1: Architecture Diagram Table- 3: Students Dataset © 2019, IRJET | Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 6750 International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 05 Issue: 09 | Sep 2018 p-ISSN: 2395-0072 www.irjet.net Chart -1: Final Grade Distribution 5. CONCLUSION In this paper, the Logistic Regression is employed for students' data to predict the students' performance. This study can facilitate the students and the professors to motivate the students of all categories to perform well. This study helps to identify students who require special attention, minimize the failure rate and take relevant action to succeed in the upcoming semester examination. Future work includes employing big data to help decision makers in education sector to make better and more intelligent decision in education big data environment. REFERENCES [1] J.K.Jothi and K.Venkatalakshmi, "Intellectual performance analysis of students by using data mining techniques", International Journal of Innovative Research in Science, Engineering and Technology, vol 3, Special iss 3, March 2014. [2] Nikitaben Shelke and Shriniwas Gadage, "A survey of data mining approaches in performance analysis and evaluation", International Journal of Advanced Research in Computer Science and Software Engineering , vol 5, iss 4, 2015K. [3] M.S. Mythili and A.R.Mohamed Shanavas , "An analysis of students' Performance using classification algorithms ", IOSR-JCE, Volume 16, iss1, Jan. 2014. [4] A.Dinesh Kumar and V.Radhika, "A survey on predicting student performance", International Journal of Computer Science and Information Technologies, Vol. 5, 2014. [5] E. Osmanbegović and M. Suljić, '"Data mining approach for predicting students performance", Economic Review, vol 10, iss 1, 2012. [6] Sayali Rajesh Suyal and Mohini Mukund Mohod, "Quality improvisation of student performance using data mining techniques", International Journal of Scientific and Research Publications, vol 4,iss 4, April 2014. [7] OTOBO Firstman Noah, BAAH Barida and Taylor Onate Egerton, "Evaluation of student performance using data mining over a given data space", International Journal of Recent Technology and Engineering (IJRTE) ISSN: 2277-3878, Volume-2, iss 4, September 2013. [8] Brijesh Kumar Baradwaj and Saurabh Pal, "Mining educational data to analyze Students' performance", (IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 2, No. 6, 2011. © 2019, IRJET | Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 6751 International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 05 Issue: 09 | Sep 2018 p-ISSN: 2395-0072 www.irjet.net [9] T.Jeevalatha, N. Ananthi and D.Saravana Kumar, "Performance analysis of undergraduate students placement selection using Decision Tree Algorithms", International Journal of Computer Applications (0975- 8887), vol 108, December 2012. [10] Ryan J.D.B.Baker and Kalina Yacef , "The state of educational data mining in 2009: A review and future revisions", Journal of Educational Data Mining , Vol.1,No.1, February 2009. [11] A.F . ElGamal, "An educational data mining model for predicting student performance in programming course", International Journal of Computer Applications(0975-8887), Vol.70, No.17, May 2013. [12] D.Magdalene Delighta Angeline, "Association rule generation for student performance analysis using Apriori Algorithm", The SIJ Transactions on Computer Science Engineering And Applications(CSEA), vol.1, March-April 2013. [13] Bhise R.B, Thorat S.S and Supekar A.K, "Importance of data mining in higher education system", IOSR Journal of Humanities and Social Science, ISSN: 2279-0837, vol.6, iss 6, January-February 2013. [14] V.Ramesh, P.Parkavi and P.Yasodha, "Performance analysis of data mining techniques for placement chance prediction", International Journal of Scientific and Egineering Research , Vol.2, iss 8, August 2011. [15] Mohammed M.Abu Tair and Alaa M.El-Halees, 'Mining Educational Data to Improve Students' Performance: A case Study', International journal of information and Communication Technology Research, ISSN: 2223-4985, vol.2 no.2, February 2012 . © 2019, IRJET | Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 6752


text:
- Wireless Vibration Alarm triggered by vibration, suitable for Bikes, Motorcycles, Vehicle, Doors, Windows or anthing you wish to protect, detering thieves effectively by 110dB loud alarm
- Vibration alarm with remote control, 4 mode--Arm/Disarm/Panic/Doorbell mode conveniently controlled by the remote, remote control range up to 82ft
- 10 levels adjustable sensitivity from gently touch to push or beat, you can easily set it according to your need
- Low consumption battery can last longer, the wireless bike alarm system can continuous standby over one year(according to on/off five times a day)
- Easy operation and installation, installation by pasting with double-sided sticker or tying it with strap
Product Description
Wsdcam wireless bike/motorcycle/door/window security alarm with remote
Wsdcam Wireless Vibration Alarm triggered by vibration, suitable for Bikes, Motorcycles, Vehicle, Doors, Windows or anything you wish to protect, deterring thieves effectively by 110dB loud alarm. (please know this vibration alarm does not need magnetic sensor part because it's NOT a magnetic alarm it's a vibration alarm)
- Wireless vibration alarms with remote by detecting gentle touch, push or beat (vibration triggered)
- Remote control range up to 25M
- 10 levels adjustable sensitivity
- Add more remotes and alarms. You can program multi remotes for one alarm, also can program one remote for multi alarms.
- Low-consumption design,which makes it can work at least a year
- Alarm need two AAA batteries to operate ( not included )
- Remote need a 12V/27A alkaline batteries to operate ( included )
- 4 Mode-- Arm/Disarm/Panic/Doorbell mode
- Not only a safety/antitheft alarm, but also a Doorbell, in doorbell mode, the vibration alarmdetects vibration the host sound"Dingdong"
- Low battery warning
- Package Size: 4.2*2.5*1.5 inch, Package Weight: 71g
Package Including:
1* Alarm, 1* Remote Control, 1* User Manual
10 Levels Adjustable Sensitivity
The factory default is the highest sensitivity (the first level). Please set it before use according to your need.
1. Press and hold the Disarm key of the remote control (Ignore the prompt tone). The remote control's LED turns off for about 3 seconds and lights up again after 3 seonds, then release the Disarm key and press the doorbell key to adjust the sensitivity level.
2. When it's in the highest sensitivity, the alarm will sound a long "Bi......". Press the
doorbell key once, the sensitivety level turns to the next level and the alarm sound a short "Bi......". When it's in the tenth level, if press one more time, it turns to the first level. The higher the level the less sensitive.
Remote Control 4 Mode
- Arm key: Press it, once triggered, alarm will bell 110db voice.
- Disarm key: Press it, the arm status or loud voice will stop.
- SOS key: Press it, the alarm will bell 110db voice though alarm is not triggered. This function will be very helpful when someone is in danger or need emergent help.
- Doorbell key: Press it, once triggered, it will bell "dingdong" to alert you someone is coming in.
Please Note
1) You would better install the vibration alarm in somewhere get away from the rain/hot sun. For example, under the bicycle seat. Or it better to put the vibartion alarm in a plastic bag before you install it.
2) The internal vibration sensor is located at horizontal position, we recommend you install it in a horizontal direction to obtain a best effect.
3) Please know that the vibration alarm is not included door sensor because it does not need.
Simple Installation
Vibration Alarm come with double-sided adhesive, just peel and stick to install them on bicycle, motorcycle, doors, windows or anywhere.
Pair More Alarms &Remotes
Take batteries out of alarm
Put batteries into alarm, long press bell button on remote immediately
Press arm key once after you hear "Bi", alarm will sound two "Bi.Bi". Done
Multipurpose Use
Not only suitable for Bikes, Motorcycles, Vehicle, but also designed for home, single apartment, rental housing, office, hotel, shop, mall and residential house.
Payment & Security
Payment methods
Your payment information is processed securely. We do not store credit card details nor have access to your credit card information.

subset: commoncrawl


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By now most everyone is familiar with augmented reality (AR) in try on and try out applications as well as with its use of data tags and overlays that pop up over virtual and real objects, providing details and actions. Any one can see real and virtual items at the same time whether on a smartphone, with glasses, or googles.
Today, many brands are interested in using or are already starting to use AR for marketing in the metaverse. Online stores today use AR-based product visualizations that deliver interactive product experiences that connect with customers on an emotional level and potential customers can use the technology to check out products from practically any location. For businesses, moving augmented reality from online shopping to the metaverse may be the incentive needed to move AR-based marketing to the masses.
Augmented Reality in ecommerce
Most people have already experienced AR technology through a smartphone app with over 50% of smartphone owners claiming to have already used AR apps when shopping.
In this post we will define and focus on augmented reality as a virtual interactive experience of a real-world environment where real objects are enhanced by digitalized perceptual information, and virtual objects can also appear within the real-world environment.
AR enables brands to create an online store where shoppers can move around and interact with virtual and real objects. For marketers, AI enables the creation of new advertising and product interaction experiences such as virtual storefronts, signs and billboards that are visible to all who are within a defined area of the metaverse.
Why Augmented Reality?
When using augmented reality the real world becomes metaverse-enhanced and doesn't require people to leave reality to visit. That's a key reason why AR and its blended real world and virtual world experience will likely become a part of our own world.
The viewing experience is also more relaxed and more palatable to most people than say a VR experience as there are few people who are willing to stare for hours at screens a half-inch from their eyes and remain detached from the world around them.
Metaverse AR worlds include radically different elements from those in real life. These AR worlds have persistence and immersion and give one a sense of presence. The view is a first-person perspective with a few differences available for personal preference, however, by and large what one person sees and experiences all others do as well. Being cut off from our surroundings and unable to see or hear is considered by most as being unnatural, dangerous, and disconcerting.
AR enables users to see and remain a part of their surroundings rather than cutting them off entirely and an AR user experiencing the metaverse can still see others in the room and be aware of when someone is nearby.
AR as a Marketing Tool
In order for there to be an effective AR-based marketing campaign, there must be an audience to which to market. Today you may not know anyone that regularly spends time in the metaverse or perhaps you know someone that spends a regular amount of time in a metaverse wearing AR glasses or using an AR app. Looking ahead we expect that most people will experience AR and the metaverse.
This doesn't mean that brands should wait for broad adoption of AR as now is the time to start preparing for emerging marketing trends in the current environment and in the metaverse.
Businesses and marketers need to study the trends of the metaverse now so they don't find themselves left behind when the technology is mainstream. The initial interest in the metaverse reveals a major opportunity for brands to start experimenting with sales and marketing strategies.
The widespread use of AR is here and now as the hardware has now become technologically advanced and low enough in cost to be adopted by a mass market. Widespread adoption will largely depend on whether there are enough easily identifiable use cases for AR.
AR has the potential to enable marketers to position their products in the best light possible, as it enables marketers to provide a narrative of their products in real-life and allows customers to experience this reality.
When used properly, AR is a powerful tool for businesses and marketers as it allows customers to experience products in a variety of use cases that may not be immediately obvious and it gives them the freedom of how to engage with a brand.
AR in the Metaverse
Consumers have high expectations for brand experiences in the metaverse and they expect to be receiving more dynamic, enriching product information in this new environment. The brands that will win out are those that are willing to try out the technology now, so they can deliver positive experiences when consumers start rushing to join. The biggest advantage of using AR is that it opens up a world of possibilities that don't exist in real life and it is available to use now.
A consumer can create virtual artwork that appears on the wall or ceiling, and may be animated or continually changing and is always available for anyone with AR glasses to see and engage.
For those skittish about needles perhaps virtual tattoos will be more to their liking and yes, they may be animated. As consumers walk down a store aisle, they may look at a product to see an AR enabled product descriptions, pricing, available accessories, and so forth.In your home, instead of sitting in a particular room with a TV, one could sit anywhere, indoors or outdoors, to watch a movie on a wall-sized screen. When working on your car, you could look at the area being worked on to see a schematic overlay. For almost any item a person can purchase, they can use AR to view it in their home or on their body.
This metaverse vision is about being in real places where mixed reality glasses can reveal otherwise invisible digital layers. For businesses, marketers and advertisers, these types of scenarios present opportunities to interact with prospects and customers.
Currently, many brands use augmented reality to enhance their marketing efforts through the use of AR-enhanced mobile apps that allows potential customers to try out clothing, makeup and hairstyles on themselves or see a piece of furniture in their own homes.
Many consumers have stated that they prefer shopping online over in person, however, there is an element missing from much of today's online shopping and that is the act of trying on, and interacting with a product before you buy it. AR allows consumers to experience and interact with a product before purchasing it, creating a strong bond between the buyer and the product. Consumers can do that today with current AR experiences and that capability will become even more rich in the metaverse.
In stores, retail spaces, online and in the metaverse, shoppers can use virtual try-on filters to simulate how they might look any type of apparel (hats, shoes, watches and jewelry) without visiting a store. This allows users to purchase with confidence, leading to greater customer satisfaction and far fewer returns and exchanges. The same principles can also be applied to furniture, automobiles, electronics, appliances, testing out the product or seeing how it looks or will fit in your home.
Augmented reality can radically change how we view the world around us. The full potential of AR is staggering and, when combined with the metaverse, offers unlimited opportunities for marketers to take advantage of its unique properties.
The broader metaverse has the potential to impact the world of marketing because it is not limited to a singular big tech or gaming platform nor is it reliant on proximity or attachment to a computer. It is inclusive of every connected place, person, device and space.
Contact us today for a free consultation or visit our website to learn more about how we can help you improve your customers' experience with product configuration and visualization.
If you are wondering what you can do with Zakeke, check out our DEMO STORE.
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