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Imperial Journal of Interdisciplinary Research (IJIR) Vol-2, Issue-12, 2016 ISSN: 2454-1362, http://www.onlinejournal.in Recognition of Slow Learners Using Classification Data Mining Techniques Mukesh Kumar1, Shankar Shambhu2 & Punam Aggarwal3 1,2 3 Chitkara University, HP (INDIA) Smt. Aruna Asaf Ali Govt. P. G. College, Kalka (INDIA) Abstract: Educational Data Mining is used to predict the future learning behavior of the student. It is still a research topic for the researcher who wants do better result from the prediction of the student. The results of all these techniques help the teachers, management, and administrator to draft new rules and policy for the improvement of the educational standards and hence overall results and student retention. Taking this point in mind work has been done to find the slow learner in a High School class and then provide timely help to them for improving their overall result. There are lots of techniques of data mining are available for use but we are selecting only those techniques which are mostly used by different research for their result prediction like J48, REPTree, Naive Bayes, SMO, Multilayer Perceptron. On the collected dataset Multilayer Perception classification algorithm gives 87.43% accuracy when using whole dataset as training dataset and SMO and J48 gives 69.00% accuracy when using 10-fold cross validation algorithm. Keywords: Data Mining, Educational Prediction, Classification, Clustering. DM, 1. Introduction Educational data mining is one of the applications of data mining. Data mining is used to find the hidden pattern from a huge dataset and then apply that hidden patterns for the decision making in future. Its application is not limited to education but also covers fields like sales, retail, transportation, sports, marketing etc. In education, these data mining techniques are used to predict slow learners, dropout, under-performer etc and hence provide timely help to those students who are the problem in education. EDM is also broadly applied to E-learning system, classroom teaching, MOOC Course learning, curriculum redesign analysis, distance education etc. It is a process to find some knowledge of the database and then apply that knowledge for future improvement. It is also known as KDD (Knowledge discovery in a database). There is so many tools are available in the commercial market for the mining purpose like RapidMiner, WEKA, DBMiner, Imperial Journal of Interdisciplinary Research (IJIR) Clementine, Intelligent Miner etc with the proper interface for all techniques of mining. Different research gives their own definition of education, but the overall result of each research is same i.e. How to improve the overall education system? There are lots of factors which affect the education system like the student, management, administrators, infrastructures, teachers, teaching methodology, basis facilities for boys and girls (like separate toilets for M/F), transportation facilities etc. So with the help of educational data mining techniques, detailed analyses are performed on these factors and find out those factors which affect the education of the student and hence students underperformed. 2. Liability of data mining in academics setting: As already written EDM play a significant role for the overall development of the education? With the help of this following question can be answered which are enlisted below: I. Who is the weak student in a particular class? II. Who is likely being the dropout in the education? III. Which subject students like most in their overall course? IV. Which courses most attract the student in education? V. Find out the possible attribute which effect the student education and hence performance? VI. How we can help those student who are slow learner, under-performer and problem of education dropout. VII. Most importantly predicting result of the student in final examination. 3. Proposed work for this research Education plays a crucial role in the development of the society. If education system and technology work together then it makes unbelievable growth for Page 741 Imperial Journal of Interdisciplinary Research (IJIR) Vol-2, Issue-12, 2016 ISSN: 2454-1362, http://www.onlinejournal.in Fig 1. Use of Data Mining in Education setting the society. At present technology are also used in education like E-learning, MOOC Courses, Smart Classes are introduced in school and it really works well for the overall development of the student. Technologies like data mining are also introduced in the education sector for the prediction of the student in their future learning behaviors. So taken these prospective in mind, work has been done to predict the slow learners in a class and hence provide timely help for improving their final result. The major motives behind this work are: I. Find source of data collection for creating dataset which contains predictive variables II. Selecting best data mining technologies for the analysis of the student performance. III. After analysis of the dataset identifies those students who are slow learners and need immediate help in their study. IV. Observe those variables which are extremely influencing for the prediction of the student academics performance. V. At the end compare the predictive result of all the techniques and choose the best classification algorithm result for further improvement. At the end of this paper, all these motives are fulfilled and a brief conclusion is given. It helps for further research in this novel field of data mining. 4. Literature Survey: Background and prior work in this area Use of data mining in education is tremendous. But still, lots of researchers are working on educational data mining techniques for the betterment of education. As already told it's a broad field and not limited to the present discussion like the prediction of slow learners in a class. Fig 2: Data Mining Process to be taken under consideration Han Jiawei and Micheline Kamber, Education Data Mining is a process of Knowledge Discovery in a huge database which consists of Cleaning, Imperial Journal of Interdisciplinary Research (IJIR) Integration, Selection, Transformation of data and pattern Evaluation Phases [1]. Page 742 Imperial Journal of Interdisciplinary Research (IJIR) Vol-2, Issue-12, 2016 ISSN: 2454-1362, http://www.onlinejournal.in S Weiss et al, They explain data mining as search techniques, which search for the valuable information for a huge database and hence apply that information for better decision making [2]. William J. Frawley et al and Tech. Forecast, Data Mining is a detailed process of extracting useful pattern or useful information which is formerly unknown to the database user. The known pattern or information may include information like association rules between variables, pattern finding between variables etc. [3] [4]. S. Pal et al, they are using linear regression technique for their analysis and find that factors like mother’s education and family income of students affect their academic performance [5]. M Bala, Dr. DB Ojha, They define EDM as techniques which are helpful in finding the unknown facts from a larger database, which are impossible to find manually and hence that information, are effectively used in the education setting. It is used to increase the student retention rate, improve the educational standard, and help administrator for setting new rules and regulation for improving educational standard [6]. Ying Zhang et al, to extract useful information, data mining used to combine the machine learning, visualization, and statistical approaches. There are lots of techniques are adopted to collect the data for making dataset for analysis like questionnaires, feedback form, interview, discussion. After collecting all data make a dataset according to the selected tool for analysis and then apply some techniques for analysis like Classification, Clustering, Linear Regression, Support Vector Machine, Decision Tree, Naive Bayes and K-mean. Student learning behaviors, Course learning, student retention rate, course suitability etc are predicted using data mining techniques [7]. Cortez and Silva, taken twenty-nine attributes for prediction of the result in Mathematics and Portuguese. They applied data mining algorithm (like Decision Tree, Neural Network, Support Vector Machine and Random forest) on the dataset of 788 students of two schools from the Alentejo region of Portugal. After analysis, they found that Decision Tree (DT) and Neural Network (NN) had 93% and 91% accuracy in predicting the result according to two- class (pass/fail) respectively [8]. Galit.et.al, in his case study they analyze the student’s data for predicting their future learning behaviour and hence the result. They also predict the student result and warn them that they are at risk of failure in final examination and provide timely help to them [9]. M. Ramaswami et al, for the analysis of the educational outcome of the student’s in higher secondary education they used CHAID prediction techniques to find the interrelationship between Imperial Journal of Interdisciplinary Research (IJIR) different variables which are used for the prediction. They used seven different class predictor variables for their experimentation [10]. Lars Schmidt-Thieme et al, they applied machine learning algorithm for prediction, a result of which was further used to improve the academic performance of the student. To deal with the problem of the imbalanced data they applied three different methods and hence found satisfactory results. After balanced the datasets they further used SVM for a small dataset and Decision tree algorithm for larger dataset [11]. V. Ramesh et al, they applied survey methodology to make the final dataset with some significant variable of students and with experimental methodology tried to found only those variables which influencing the final result of the student. They applied SMO, J48, REPTree, Naive Bayes and Multilayer perception techniques for their experimentation purpose. After analysis, they found that factor like parent’s occupation plays a very important role in student performance [12]. Applying EDM techniques for knowledge discovery is important for the teachers, management, and student. They all are using this knowledge for the improvement of the education system. Teachers are using this knowledge for improving their teaching standard and the student is using to improving their learning skill. Management of the institution is using this knowledge for improving infrastructure standard, provide basic facilities to the student and decision making. 5. Data collection and proposed methodology By Han Jiawei, Micheline Kamber, EDM software's should be developed in such way that the users can analyze the student data with different dimensions, enables to categorize and summarize the desired results [2]. To complete this work a survey was conducted on student and then for analysis purpose a data mining tool should be used. Here WEKA tool is used for the analysis purpose because it is open source software and almost all the data mining techniques are implemented on it. After the detailed survey and having the discussion with the experts, some attributes are selected related to the students which are mostly affecting the academic performance in high schools. These attributes are also known as input variables for the analysis of the dataset. The data are collected from two different high schools with the help of survey method and after that put it in desired file format required for the analysis. Page 743 Imperial Journal of Interdisciplinary Research (IJIR) Vol-2, Issue-12, 2016 ISSN: 2454-1362, http://www.onlinejournal.in Table-1: Selected Attributes of student taken for analysis purpose S. NO ATTRIBUTE DESCRIPTION DOMAIN VALUES OF THE ATTRIBUTES 1 TY_HS Types of High School { Govt, Private, Govt_add} 2 TY_BRD Types of Education board {State Board, CBSE, ICSE} 3 MED_INS Medium of Instruction {Hindi, English, Pahari} 4 TY_SCL Type of School {Boys, Girls, Co-education} 5 GND Gender of student {Girls, Boys} 6 PRI_TUT Private Tuition taken {Yes, No} 7 AR_SCL Location of the school area {Urban, Rural} 8 INT_GRD Internal Grade of student {A, B, C, D, E, F} 9 MOB Mobile Phone {Yes, No} 10 COM Computer at Home {Yes, No} 11 INT_ACC Internet access to student {Yes, No} 12 ATND Attendance in the school { In % age out of attendance taken} 13 CLASS Eligible of Not Eligible {E, NE} Here for the implementation purpose CSV file format are used for WEKA tool. WEKA is a open source software tool kit and support maximum of classification, clustering and association rule algorithms 6. Implementation of EDM techniques on dataset During this phase of work, first of all we preprocess our dataset with the help of WEKA Preprocess feature on the tool interface. Apply Filters on the dataset: For pre-processing of dataset, implement filters on it to remove those attributes who are not supposed to be significant for the result prediction. After implementation of filter, dataset left with only eight different attributes along with class. The removed attributes are types of High for the classification of the dataset with two class values. Most of the algorithms are used ranker search for find the high potential attributes in the given Imperial Journal of Interdisciplinary Research (IJIR) School, types of education board, medium of instruction and type of school. Find out the High Potential Attribute: After pre-processing of dataset find the high potential attribute which are critically affects the overall dataset with different attribute selection method. In WEKA the different attribute evaluator algorithm are CorrelationAttributeEval, GainRatioAttributeEval, InfoGainAttributeEval, OneRAttributeEval, PrincipalComponents, ReliefAttributeEval, and SymmetricalUncertAttributeEval which are further used different search method like BestFirst, GreedyStepwise and Ranker. In this work all the dataset are used as a training dataset and we are not using 10-fold cross validation method because of less data in dataset. After applying all algorithm of attribute evaluator INT_GRD, INT_ACC and ATND are found most important dataset. In table below the entire algorithm with their search method and first rank attribute are mentioned. Page 744 Imperial Journal of Interdisciplinary Research (IJIR) Vol-2, Issue-12, 2016 ISSN: 2454-1362, http://www.onlinejournal.in Table-2: High Potential attributes selection form the dataset ATTRIBUTE EVALUATOR SEARCH METHOD FIRST RANK ATTRIBUTE cfsSubsetEval CorrelationAttributeEval GainRatioAttributeEval InfoGainAttributeEval OneRAttributeEval ReliefFAttributeEval SymmetricalUncertAttributeEval GreedyStepwise Ranker Ranker Ranker Ranker Ranker Ranker, BestFirst INT_GRD INT_ACC INT_GRD INT_GRD ATND INT_GRD INT_GRD At the end of this section, it is clear that only seven attributes in the dataset are useful for the prediction of the class attribute and rest of the attributes are not affecting the overall result of the analysis. 7. Results of implementation classification algorithm like Naive Bayes, SMO, J48, REPTree and Multilayer Perceptron. These entire algorithms are also tested with 10 fold cross validation check as well as using full training data set. The Correctly and Incorrectly classified Instances after implementing listed algorithm under 10-fold validation are given in table below: After the completed the pre-processing task, dataset is tested and analyzed with five well known Table-3: Correctly & Incorrectly Classified Instances using 10 fold validation check Data Mining Techniques used Multilayer Perceptron Naive Bayes J48 SMO REPTree OneR ZeroR Correctly Classified Instances 57.2864 % 67.3367 % 69.3467 % 69.3467 % 67.8392 % 67.8392 % 69.3467 % Using 10-fold validation check classification algorithm like J48, SMO and ZeroR are performing better than other algorithm under consideration. The Incorrectly Classified Instances 42.7136 % 32.6633 % 30.6533 % 30.6533 % 32.1608 % 32.1608 % 30.6533 % correctly classified instances are 69.3 percent. Which is acceptable as the baseline condition given by ZeroR algorithm is also 69.3 percent? Fig - 3: Comparison of classifiation accuracy with the help of graph Imperial Journal of Interdisciplinary Research (IJIR) Page 745 Imperial Journal of Interdisciplinary Research (IJIR) Vol-2, Issue-12, 2016 ISSN: 2454-1362, http://www.onlinejournal.in The Correctly and Incorrectly classified Instances after implementing listed algorithm using full dataset as training dataset are given in table below: Table-4: Correctly & Incorrectly Classified Instances using training data set Data Mining Techniques used Multilayer Perceptron Naive Bayes J48 SMO REPTree OneR ZeroR Correctly Classified Instances 87.4372 % 69.3467 % 69.3467 % 69.3467 % 72.3618 % 69.3467 % 69.3467 % Using full dataset as training dataset for classification algorithm like Multilayer Perceptron Incorrectly Classified Instances 12.5628 % 30.6533 % 30.6533 % 30.6533 % 27.6382 % 30.6533 % 30.6533 % are performing exceptional well with 87.43 percent correctly classified instances. Fig - 3: Comparison of classification accuracy with the help of graph medium of instruction. May be these attribute also 8. Conclusion and future scope affect the performance of the student in education. There are lot of drawbacks in education system like midterm evaluation system use. It is really not 9. References understood that why midterm evaluation is taken throughout the year. In this particular paper, [1]Han Jiawei, Micheline Kamber, “Data Mining: classification algorithms are applied on dataset of Concepts and Technique”. Morgan Kaufmannv Publishers, students, to predict slow learners in a class. For that a 2000. model was created based selected attribute of [2]Weiss S. & Indurkhya N, “Predictive Data Mining: A student. From so many classifiers algorithm Practical guide”, Morgan Kauf-. Mann, 1998. Multilayer Perceptron algorithms gave an accuracy of 87.44%, when using whole data as a training data. 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