Commenced in January 2007
Paper Count: 31819
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.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1074948Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2591
 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.
 W. Hamalainen, "Descriptive and Predictive Modelling Techniques for Educational Technology", Thesis Department of Computer Science, University of Joensuu, Finland.
 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.
 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.
 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.
 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.
 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.
 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.
 S. B. Kotsiantis and P. E. Pintelas, "Local voting of weak classifiers", KES Journal. 3(9):pp. 239-248, 2005.
 Weka, University of Waikato, New Zealand, http://www.cs.waikato.ac.nz/ml/weka.
 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.
 H. Zhang, L. Jiang, J. Su, "Hidden naive Bayes", American Association for Artificial Intelligence. AAAI pp. 919-924, 2005.