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Predicting Learning Behavior of Students using Classification Techniques

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2016
K. Prasada Rao, M.V.P. Chandra Sekhara Rao, B. Ramesh

Prasada K Rao, Chandra Sekhara M V P Rao and B Ramesh. Article: Predicting Learning Behavior of Students using Classification Techniques. International Journal of Computer Applications 139(7):15-19, April 2016. Published by Foundation of Computer Science (FCS), NY, USA. BibTeX

	author = {K. Prasada Rao and M.V.P. Chandra Sekhara Rao and B. Ramesh},
	title = {Article: Predicting Learning Behavior of Students using Classification Techniques},
	journal = {International Journal of Computer Applications},
	year = {2016},
	volume = {139},
	number = {7},
	pages = {15-19},
	month = {April},
	note = {Published by Foundation of Computer Science (FCS), NY, USA}


The main objective of any educational organization is to provide quality education and improve the overall performance of an institution by looking at individual performances. One way to analyze learners' performances is to identify the areas of weakness and guide their students to a better future. Although data mining has been successful in many areas, its use in student performance analysis is still relatively new, i.e. the knowledge is hidden in educational data set and it is extracted using data mining techniques. This paper discusses about a learning model for predicting student performance using classification techniques. Also the paper shows the comparative performance analysis of J48, Naïve Bayesian classifier and Random forest algorithm.


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Educational Data Mining, Random forest, Classification