A LITERATURE REVIEW FROM 2011 TO 2014 ON STUDENT’S ACADEMIC PERFORMANCE PREDICTION AND ANALYSIS USING DECISION TREE ALGORITHM

Hardeep Kaur

Abstract


Abstract—Success of any educational institute depends upon the success of the students of institute. Student’s performance prediction and its analysis are essential for improvement in various attributes of students like final grades, attendance etc. This prediction helps teachers in identification of weak students and to improve their scores. Various data mining techniques like classification, clustering, are used to perform analysis. In this paper implementation of various decision tree algorithms ID3, J48/C4.5, random tree, Multilayer Perception, Rule Based and random forest have been studied for student’s performance prediction and analysis. The WEKA tool is used to perform evaluation. To evaluate the performance percentage split method or cross validation method is used. Main objective behind this analysis is to improve student’s performance. This review paper explores the use of various decision tree algorithms for student’s academic performance prediction and its analysis.

Keywords—EDM, Decision tree, J48, random tree, ID3, Multilayer Perception, CART, IBI


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