Improving Academic Performance Prediction using Voting Technique in Data Mining
In this paper we compare the accuracy of data mining
methods to classifying students in order to predicting student-s class
grade. These predictions are more useful for identifying weak
students and assisting management to take remedial measures at early
stages to produce excellent graduate that will graduate at least with
second class upper. Firstly we examine single classifiers accuracy on
our data set and choose the best one and then ensembles it with a
weak classifier to produce simple voting method. We present results
show that combining different classifiers outperformed other single
classifiers for predicting student performance.
[1] L. Hall, K. Bowyer, W. Kegelmeyer, T. Moore, C. Chao (2000)
"Distributed learning on very large data sets". In: Proceedings of the
Sixth ACM SIGKDD International Conference on Knowledge
Discovery and Data Mining, pp. 79-84.
[2] W. Hamalainen, "Descriptive and Predictive Modelling Techniques for
Educational Technology", Thesis Department of Computer Science,
University of Joensuu, Finland.
[3] W. Hamalainen, and M. Vinni, "Comparison of machine learning
methods for intelligent tutoring systems", In proceedings of the 8th
International Conference on Intelligent Tutoring Systems, Jhongli,
Taiwan, pp. 525-534, June 2006.
[4] S. B. Kotsiantis, C. J. Pierrakeas, and P. E. Pintelas, "Preventing student
dropout in distance learning using machine learning tachniques", In
proceedings of 7th International Conference on Knowledge-Based
Intelligent Information and Engineering Systems (KES 2003), pp. 267-
274, 2003. ISBN 3-540-40803-7.
[5] B. Minaei-Bidgoli, Kashy, D. A. Kortemeyer, G. and Punch, W. F.
"Predicting student performance: an application of data mining methods
with an educational web-based system", 33rd Annual Conference on
Frontiers in Education (FIE 2003), vol. 1, pp. 13-18, 2003. DOI:
10.1109/FIE.2003.1264654.
[6] B. Minaei-Bidgoli, G. Kortemeyer, and W. F. Punch, "Enhancing Online
Learning Performance: An Application of Data Mining Method", In
proceedings of The 7th IASTED International Conference on Computers
and Advanced Technology in Education (CATE 2004), Kauai, Hawaii,
USA, pp. 173-8, August 2004.
[7] Nguyen Thai Nghe, P. Janecek, and P. Haddawy, "A comparative
analysis of techniques for predicting academic performance",
ASEE/IEEE Frontiers in Education Conference, pp. T2G7-T2G12, 2007.
[8] C. Romero, S. Ventura, P. G. Espejo, and C. Hervas, "Data Mining
Algorithms to Classify Students", 1st International Conference on
Educational Data Mining, Montreal, Quebec, Canada, 2008. ISBN-
13:9780615306292.
[9] S. B. Kotsiantis and P. E. Pintelas, "Local voting of weak classifiers",
KES Journal. 3(9):pp. 239-248, 2005.
[10] Weka, University of Waikato, New Zealand,
http://www.cs.waikato.ac.nz/ml/weka.
[11] W. Zang, and F. Lin, "Investigation of web-based teaching and learning
by boosting algorithms". In Proceedings of IEEE International
Conference on Information Technology: Research and Education (ITRE
2003), pp. 445-449, 2003.
[12] H. Zhang, L. Jiang, J. Su, "Hidden naive Bayes", American Association
for Artificial Intelligence. AAAI pp. 919-924, 2005.
[1] L. Hall, K. Bowyer, W. Kegelmeyer, T. Moore, C. Chao (2000)
"Distributed learning on very large data sets". In: Proceedings of the
Sixth ACM SIGKDD International Conference on Knowledge
Discovery and Data Mining, pp. 79-84.
[2] W. Hamalainen, "Descriptive and Predictive Modelling Techniques for
Educational Technology", Thesis Department of Computer Science,
University of Joensuu, Finland.
[3] W. Hamalainen, and M. Vinni, "Comparison of machine learning
methods for intelligent tutoring systems", In proceedings of the 8th
International Conference on Intelligent Tutoring Systems, Jhongli,
Taiwan, pp. 525-534, June 2006.
[4] S. B. Kotsiantis, C. J. Pierrakeas, and P. E. Pintelas, "Preventing student
dropout in distance learning using machine learning tachniques", In
proceedings of 7th International Conference on Knowledge-Based
Intelligent Information and Engineering Systems (KES 2003), pp. 267-
274, 2003. ISBN 3-540-40803-7.
[5] B. Minaei-Bidgoli, Kashy, D. A. Kortemeyer, G. and Punch, W. F.
"Predicting student performance: an application of data mining methods
with an educational web-based system", 33rd Annual Conference on
Frontiers in Education (FIE 2003), vol. 1, pp. 13-18, 2003. DOI:
10.1109/FIE.2003.1264654.
[6] B. Minaei-Bidgoli, G. Kortemeyer, and W. F. Punch, "Enhancing Online
Learning Performance: An Application of Data Mining Method", In
proceedings of The 7th IASTED International Conference on Computers
and Advanced Technology in Education (CATE 2004), Kauai, Hawaii,
USA, pp. 173-8, August 2004.
[7] Nguyen Thai Nghe, P. Janecek, and P. Haddawy, "A comparative
analysis of techniques for predicting academic performance",
ASEE/IEEE Frontiers in Education Conference, pp. T2G7-T2G12, 2007.
[8] C. Romero, S. Ventura, P. G. Espejo, and C. Hervas, "Data Mining
Algorithms to Classify Students", 1st International Conference on
Educational Data Mining, Montreal, Quebec, Canada, 2008. ISBN-
13:9780615306292.
[9] S. B. Kotsiantis and P. E. Pintelas, "Local voting of weak classifiers",
KES Journal. 3(9):pp. 239-248, 2005.
[10] Weka, University of Waikato, New Zealand,
http://www.cs.waikato.ac.nz/ml/weka.
[11] W. Zang, and F. Lin, "Investigation of web-based teaching and learning
by boosting algorithms". In Proceedings of IEEE International
Conference on Information Technology: Research and Education (ITRE
2003), pp. 445-449, 2003.
[12] H. Zhang, L. Jiang, J. Su, "Hidden naive Bayes", American Association
for Artificial Intelligence. AAAI pp. 919-924, 2005.
@article{"International Journal of Information, Control and Computer Sciences:59345", author = "Ikmal Hisyam Mohamad Paris and Lilly Suriani Affendey and Norwati Mustapha", title = "Improving Academic Performance Prediction using Voting Technique in Data Mining", abstract = "In this paper we compare the accuracy of data mining
methods to classifying students in order to predicting student-s class
grade. These predictions are more useful for identifying weak
students and assisting management to take remedial measures at early
stages to produce excellent graduate that will graduate at least with
second class upper. Firstly we examine single classifiers accuracy on
our data set and choose the best one and then ensembles it with a
weak classifier to produce simple voting method. We present results
show that combining different classifiers outperformed other single
classifiers for predicting student performance.", keywords = "Classification, Data Mining, Prediction,Combination of Multiple Classifiers.", volume = "4", number = "2", pages = "304-4", }