Paper title: Data mining technique for e-learning
Published in: Issue 2, (Vol. 10) / 2016Download
Publishing date: 2016-10-20
Pages: 26-31
Author(s): IONIŢĂ Irina
Abstract. Data Mining (DM), sometimes called Knowledge Discovery in Databases (KDD), is a powerful new technology with great potential to help companies focus on the most important information in the data they have collected via transactions. In the education field, the prediction of students learning performance, detection of inappropriate learning behaviours, and development of student profile may be considered e-learning problems where data mining can successfully solve them. In this paper, the authoress analyses the possibilities to apply data mining techniques in e-learning context, to predict the students’ status referring to their activities and the interest in using advanced tutoring tools. The experiments were performed on the basis of data provided by an e-learning platform (Moodle) regarding the logging parameters of students enrolled on Interactive Tutoring Systems discipline during the second semester of current year.
Keywords: E-learning, Data Mining, Decision, Classification, Regression.

1. J.E. Beck, B.P. Woolf, High-Level Student Modeling with Machine Learning, Gauthier, G., et al. (eds.): Intelligent Tutoring Systems, ITS 2000. Lecture Notes in Computer Science, Vol. 1839. Springer-Verlag, Berlin Heidelberg New York, 2000, pp. 584-593.

2. F. Castro, A. Vellido, A. Nebot, F. Mugica, Applying Data Mining Techniques to e-Learning Problems, Studies in Computational Intelligence, Volume 62, 2007, pp. 183-221.

3. K. Chu, M. Chang, Y. Hsia, Designing a Course Recommendation System on Web based on the Students’ Course Selection Records, World Conference on Educational Multimedia, Hypermedia and Telecommunications, 2003, pp.14-21.

4. W. Cohen, Fast effective rule induction, in Proceedings of the 12th International Conference on Machine Learning, Lake Tahoe, Calif, USA, 1995, pp. 115–123

5. M.F. Costabile, A. De Angeli, T. Roselli, R Lanzilotti, P. Plantamura, Evaluating the Educational Impact of a Tutoring Hypermedia for Children, Information Technology in Childhood Education Annual, 2003, pp. 289-308.

6. M. Feng, N. Heffernan, K. Koedinger, Looking for Sources of Error in Predicting Student’s Knowledge, The Twentieth National Conference on Artificial Intelligence by the American Association for Artificial Intelligence, AAAI’05, Workshop on Educational Data Mining. July 9-13, Pittsburgh, Pennsylvania, 2005, pp.54-61.

7. G.J. Hwang, C.L. Hsiao, C.R. Tseng, A Computer-Assisted Approach to Diagnosing Student Learning Problems in Science Courses, Journal of Information Science and Engineering 19, 2003, pp. 229-248.

8. L. C. Jain, R. A. Tedman, D. K. Tedman, Evolution of Teaching and Learning Paradigms in Intelligent Environment, Studies in Computational Intelligence, 62, 2007, ISBN: 978-3-540-71973-1 (Print) 978-3-540-71974-8 (Online).

9. S.B. Kotsiantis, C.J. Pierrakeas, P.E. Pintelas, Predicting Students’ Performance in Distance Learning Using Machine Learning Techniques. Applied Artificial Intelligence 18(5), 2004, pp. 411-426.

10. A. Kumar, Rule-Based Adaptive Problem Generation in Programming Tutors and its Evaluation, 12th International Conference on Artificial Intelligence in Education. July 18-22, Amsterdam, 2005, pp. 36-44

11. A. Liang, W. Ziarco, B. Maguire, The Application of a Distance Learning Algorithm in Web-Based Course Delivery, Ziarko, W., Yao, Y. (eds.): Second International Conference on Rough Sets and Current Trends in Computing. Lecture Notes in Computer Science. Springer-Verlag, Berlin Heidelberg New York, 2000, pp. 338-345.

12. O. Licchelli, T.M. Basile, N. Di Mauro, F. Esposito, Machine Learning Approaches for Inducing Student Models, 17th International Conference on Innovations in Applied Artificial Intelligence, IEA/AIE 2004. LNAI Vol. 3029. Springer-Verlag, Berlin Heidelberg New York, 2004, pp. 935-944.

13. J. Manyika, M. Chui, B. Brown, J. Bughin, R. Dobbs, C. Roxburgh, A. Byers, Big Data: The Next Frontier for Innovation, Competition, and Productivity, McKinsey Global Institute, May, 2011.

14. H. Margo, Data Mining in the e-Learning Domain., Computers & Education 42(3), 2004, pp. 267-287.

15. B. Minaei-Bidgoli, W.F. Punch, Using Genetic Algorithms for Data Mining Optimization in an Educational Web-based System, Cantu, P.E., et al. (eds.): Genetic and Evolutionary Computation Conference, GECCO 2003, 2003, pp. 2252-2263.

16. G. Piatetsky-Shapiro, R.J. Brachman, T. Khabaza, W. Kloesgen, E. Simoudis, An Overview of Issues in Developing Industrial Data Mining and Knowledge Discovery Applications, In KDD, 96, pp. 89-95, 1996.

17. R.R. Quinlan, C4.5: Programs for Machine Learning, Morgan Kaufmann, San Francisco, USA, 1993.

18. M.L. Dos Santos, K. Becker, Distance Education: a Web Usage Mining Case Study for the Evaluation of Learning Sites, The 3rd IEEE International Conference on Advanced Learning Technologies, ICALT’03. IEEE Computer Society. Athens Greece, 2003, pp. 360-361.

19. T.Y. Tang, G. McCalla, Smart Recommendation for an Evolving e-Learning System: Architecture and Experiment, International Journal on e-Learning 4(1), 2005, pp. 105-12

20. R. Timofeev, Classification and regression trees (CART) theory and applications, 2004.

21. S.M. Weiss, N. Indurkhya, Predictive data mining: a practical guide, Morgan Kaufmann, 1998.

22. I.H. Witten, M. Frank, M.A. Hall, Data Mining: Practical Machine Learning Tool and Technique with Java Implementation, Morgan Kaufmann, San Francisco, USA, 3rd edition, 2011.



Back to the journal content
Creative Commons License
This article is licensed under a
Creative Commons Attribution-ShareAlike 4.0 International License.
Home | Editorial Board | Author info | Archive | Contact
Copyright JACSM 2007-2020