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http://dx.doi.org/10.3745/JIPS.04.0015

Learning to Prevent Inactive Student of Indonesia Open University  

Tama, Bayu Adhi (Department of Information Systems, Faculty of Computer Science, Sriwijaya University)
Publication Information
Journal of Information Processing Systems / v.11, no.2, 2015 , pp. 165-172 More about this Journal
Abstract
The inactive student rate is becoming a major problem in most open universities worldwide. In Indonesia, roughly 36% of students were found to be inactive, in 2005. Data mining had been successfully employed to solve problems in many domains, such as for educational purposes. We are proposing a method for preventing inactive students by mining knowledge from student record systems with several state of the art ensemble methods, such as Bagging, AdaBoost, Random Subspace, Random Forest, and Rotation Forest. The most influential attributes, as well as demographic attributes (marital status and employment), were successfully obtained which were affecting student of being inactive. The complexity and accuracy of classification techniques were also compared and the experimental results show that Rotation Forest, with decision tree as the base-classifier, denotes the best performance compared to other classifiers.
Keywords
Educational Data Mining; Ensemble Techniques; Inactive Student; Open University;
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1 D. R. Garrison, "Researching dropout in distance education," Distance Education, vol. 8, no. 1, pp. 95-101, 1987.   DOI   ScienceOn
2 D. Kember, T. Lai, D. Murphy, I. Siaw, and K. S. Yuen, "Student progress in distance education: identification of explanatory constructs," British Journal of Educational Psychology, vol. 62, no. 3, pp. 285-298, 1992.   DOI
3 N. Shin and J. Kim, "An exploration of learner progress and drop‐out in Korea National Open University," Distance Education, vol. 20, no. 1, pp. 81-95, 1999.   DOI   ScienceOn
4 M. Xenos, C. Pierrakeas, and P. Pintelas, "A survey on student dropout rates and dropout causes concerning the students in the Course of Informatics of the Hellenic Open University," Computers & Education, vol. 39, no. 4, pp. 361-377, 2002.   DOI   ScienceOn
5 Indonesia Open University, "UT in Brief," 2015; http://www.ut.ac.id/en/ut-in-brief.html.
6 E. N. Ogor, "Student academic performance monitoring and evaluation using data mining techniques," in Proceedings of the Electronics, Robotics and Automotive Mechanics Conference (CERMA 2007), Morelos, Mexico, 2007, pp. 354-359.
7 W. J. Frawley, G. Piatetsky-Shapiro, and C. J. Matheus, "Knowledge discovery in databases: an overview," AI Magazine, vol. 13, no. 3, pp. 57-70, 1992.
8 J. Han and M. Kamber, Data Mining: Concepts and Techniques, 2nd ed. Amsterdam: Morgan Kaufmann, 2006.
9 E. Turban, R. Sharda, D. Delen, and T. Efraim, Decision Support and Business Intelligence Systems, 8th ed. Upper Saddle River, NJ: Pearson Prentice Hall, 2007
10 A. Pena-Ayala, "Educational data mining: a survey and a data mining-based analysis of recent works," Expert Systems with Applications, vol. 41, no. 4, pp. 1432-1462, 2014.   DOI   ScienceOn
11 L. Breiman, "Bagging predictors," Machine Learning, vol. 24, no. 2, pp. 123-140, 1996.   DOI
12 Y. Freund and R. E. Schapire, "A decision-theoretic generalization of on-line learning and an application to boosting," Journal of Computer and System Sciences, vol. 55, no. 1, pp. 119-139, 1997.   DOI   ScienceOn
13 T. K. Ho, "The random subspace method for constructing decision forests," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 20, no. 8, pp. 832-844, 1998.   DOI   ScienceOn
14 L. Breiman, "Random forests," Machine Learning, vol. 45, no. 1, no. 5-32, 2001.   DOI
15 J. J. Rodriguez, L. I. Kuncheva, and C. J. Alonso, "Rotation forest: a new classifier ensemble method," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 28, no. 10, pp. 1619-1630, 2006.   DOI   ScienceOn
16 I. H. Witten, E. Frank, and M. A. Hall, Data Mining: Practical Machine Learning Tools and Techniques, 3rd ed. Burlington, MA: Morgan Kaufmann, 2011.
17 J. R. Quinlan, C4.5: Programs for Machine Learning. San Mateo, CA: Morgan Kauffman, 1993.
18 C. Vialardi, J. Bravo, L. Shafti, and A. Ortigosa, "Recommendation in higher education using data mining techniques," in Proceedings of the International Conference on Educational Data Mining, Cordoba, Spain, 2009, pp. 190-199.
19 A. Anjewierden, B. Kolloffel, and C. Hulshof, "Towards educational data mining: using data mining methods for automated chat analysis to understand and support inquiry learning processes," in Proceedings of the International Workshop on Applying Data Mining in e-Learning (ADML 2007), Crete, Creece, 2007, pp. 23-32.
20 S. B. Kotsiantis, C. J. Pierrakeas, and P. E. Pintelas, "Preventing student dropout in distance learning using machine learning techniques," in Proceedings of the 7th International Conference on Knowledge-Based Intelligent Information and Engineering Systems (KES2003), Oxford, UK, 2003 pp. 267-274.
21 J. P. Vandamme, N. Meskens, and J. F. Superby, "Predicting academic performance by data mining methods," Education Economics, vol. 15, no. 4, pp. 405-419, 2007.   DOI   ScienceOn
22 M. Koutina and K. L. Kermanidis, "Predicting postgraduate students' performance using machine learning techniques," in Proceedings of the 7th IFIP WG 12.5 International Conference on Artificial Intelligence Applications and Innovations (AIAI2011), Corfu, Greece, 2001, pp. 159-168.
23 C. Romero and S. Ventura, "Educational data mining: a survey from 1995 to 2005," Expert Systems with Applications, vol. 33, no. 1, pp. 135-146, 2007.   DOI   ScienceOn
24 C. Romero and 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, pp. 601-618, 2010.   DOI
25 M. Skurichina and R. P. Duin, "Bagging, boosting and the random subspace method for linear classifiers," Pattern Analysis & Applications, vol. 5, no. 2, pp. 121-135, 2002.   DOI
26 C. X. Zhang and J. S. Zhang, "RotBoost: a technique for combining Rotation Forest and AdaBoost," Pattern Recognition Letters, vol. 29, no. 10, pp. 1524-1536, 2008.   DOI   ScienceOn
27 Y. Freund and R. E. Schapire, "Experiments with a new boosting algorithm," in Proceedings of the 13th International Conference on Machine Learning (ICML1996), Bari, Italy, 1996, pp. 148-156.
28 T. Fawcett, "An introduction to ROC analysis," Pattern Recognition Letters, vol. 27, no. 8, pp. 861-874, 2006.   DOI   ScienceOn