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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.


  1. Hijazi and Naive, “Factors Affecting Students’ Performance” Bangladesh e-Journal of Sociology, Volume 3. Number 1. January 2006.
  2. Tan and Vipin Kumar, “Introduction to Data Mining” Pearson, 2013.
  4. Merceron, A. and Yacef, K.,"Educational Data Mining: a Case Study" In Proceedings of the 12th International Conference on Artificial Intelligence in Education AIED 2005, Amsterdam, The Netherlands, IOS Press. 2005.
  5. Beikzadeh,M. and Delavari, N., "A New Analysis Model for Data Mining Processes in Higher Educational Systems". On the proceedings of the 6th Information Technology Based Higher Education and Training 7-9 July 2005.
  6. Weka, University of Waikato, New Zealand,
  7. Romero, C., Ventura, S. and Garcia, E., "Data mining in course management systems: Moodle case study and tutorial". Computers & Education, Vol. 51, No. 1. pp. 368-384. 2008.
  8. Minaei-Bidgoli B., Kashy, D. Kortemeyer G., Punch W., "Predicting Student Performance: An Application of Data Mining Methods with an Educational Web-Based System". In the Processing of 33rd ASEE/IEEE conference of Frontiers in Education. 2003.
  9. Ch.Ravi Kishore, K.Prasada Rao, “Performance Evaluation of Entropy and Gini using Threaded and Non Threaded ID3 on Anaemia Dataset” 2015 IEEE DOI 10.1109/CSNT.2015.112
  10. Waiyamai,K. "Improving Quality of Graduate Students by Data Mining" Department of Computer Engineering, Faculty of Engineering, Kasetsart University, Bangkok, Thailand. 2003.


Educational Data Mining, Random forest, Classification