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Performance Prediction of Engineering Students using Decision Trees

International Journal of Computer Applications
© 2011 by IJCA Journal
Volume 36 - Number 11
Year of Publication: 2011
R. R. Kabra
R. S. Bichkar

R R Kabra and R S Bichkar. Article: Performance Prediction of Engineering Students using Decision Trees. International Journal of Computer Applications 36(11):8-12, December 2011. Full text available. BibTeX

	author = {R. R. Kabra and R. S. Bichkar},
	title = {Article: Performance Prediction of Engineering Students using Decision Trees},
	journal = {International Journal of Computer Applications},
	year = {2011},
	volume = {36},
	number = {11},
	pages = {8-12},
	month = {December},
	note = {Full text available}


Data mining can be used for decision making in educational system. A decision tree classifier is one of the most widely used supervised learning methods used for data exploration based on divide & conquer technique. This paper discusses use of decision trees in educational data mining. Decision tree algorithms are applied on engineering students’ past performance data to generate the model and this model can be used to predict the students’ performance. It will enable to identify the students in advance who are likely to fail and allow the teacher to provide appropriate inputs.


  • C. Romero, S. Ventura, “Educational data mining: A survey from 1995 to 2005”, Expert system with applications 33(2007), 135-146.
  • C. Romero, S. Ventura, “Educational Data Mining: A Review of the State of the Art”, IEEE transactions on Systems, Man, and Cybernetics-Part C: applications and Reviews, Vol.40, No. 6, November 2010.
  • J. Han, M. Kamber, Data Mining Concepts and Techniques, Second edition, Morgan Kaufmann, SanFrancisco, ISBN: 978-81-312.
  • J. R. Quinlan,“Induction of decision trees”, Machine Learning, Volume 1, Morgan Kaufmann, 1986, 81-106.
  • R. Kohavi, R. Quinlan, “Decision Tree Disovery”, In Handbook of Data Mining and Knowledge Discovery, University Press,1999.
  • K. P. Soman, S. Diwakar, V. Ajay, Insight into Data Mining-Theory and Practice, Prentice Hall of India, New Delhi, ISBN: 81-203- 2897-3.
  • V. P. Bresfelean, “Analysis and Predictions on Students’ Behavior Using Decision Trees in Weka Environment”, Proceedings of the ITI 2007 29th Int. Conf. on Information Technology Interfaces, June 25-28, 2007.
  • P. Cortez, and A. Silva, “Using Data Mining To Predict Secondary School Student Performance”, In EUROSIS, A. Brito and J. Teixeira (Eds.), 2008, pp.5-12.
  • Z. J. Kovacic, “Early prediction of student success: Mining student enrollment data”, Proceedings of Informing Science & IT Education Conference (InSITE) 2010.
  • M. Ramaswami and R. Bhaskaran, “A CHAID based performance prediction model in educational data mining”, IJCSI International Journal of Computer Science Issues, Vol. 7, Issue 1, No. 1, January 2010.
  • N. Thai-Nghe, L. Drumond, A. Krohn- Grimberghe, L. Schmidt-Thieme, “Recommender System for Predicting Student Performance”, Elsvier B.V., 2010.
  • S. Elayidom, Dr. S. M. Idikkula, J. Alexander, A. Ojha, “Applying Data mining techniques for Placement chance prediction”, International Conference on Advances in Computing, Control, and Telecommunication Technologies, 2009.
  • N. Thai Nghe, P. Janecek, and P. Haddawy, “A Comparative Analysis of Techniques for Predicting Academic Performance”, 37th ASEE/IEEE Frontiers in Education Conference, October 2007.
  • P. Bresfelean, M. Bresfelean, N. Ghisoiu, “Determining Students’ Academic Failure Profile Founded on Data Mining Methods”, Proceedings of the ITI 2008 30th International Conference on Information Technology Interfaces, June 23-26, 2008.
  • A. Merceron and K. Yacef “Educational data mining: A case study”, In Proceedings AIED, 2005, pp.467-474.
  • B. K. Baradwaj, S. Pal, “Mining Educational Data to Analyze Students’ Performance”, (IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 2, No. 6, 2011.
  • I. Witten, E. Frank, Data Mining: Practical Machine Learning Tools and Techniques, 2nd Edition, Morgan Kaufmann, San Francisco, ISBN: 0-12-088407-0.