Analysis of Student Motivation Behavior on e-Learning Based on Association Rule Mining

Authors: Kunyanuth Kularbphettong, Phanu Waraporn, Cholticha Tongsiri

Abstract:

This research aims to create a model for analysis of student motivation behavior on e-Learning based on association rule mining techniques in case of the Information Technology for Communication and Learning Course at Suan Sunandha Rajabhat University. The model was created under association rules, one of the data mining techniques with minimum confidence. The results showed that the student motivation behavior model by using association rule technique can indicate the important variables that influence the student motivation behavior on e-Learning.

Keywords: Motivation behavior, e-learning, moodle log, association rule mining.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1331361

References:

[1] E. García, C. Romero, S. Ventura, and C. Castro, "Using Rules Discovery for the Continuous Improvement of e-Learning Courses," Lecture Notes in Computer Science, 2006, Volume 4224/2006, 887-895.
[2] J. Mostow, J. Beck, H. Cen, A. Cuneo, E. Gouvea, and C. Heiner, "An educational data mining tool to browse tutor-student interactions: Time will tell", In Proceedings of the Workshop on Educational Data Mining, Pittsburgh, USA (pp. 15-22), 2005.
[3] M. E. Zorrilla, E. Menasalvas, D. Marin, E. Mora, and J.Segovia, "Web usage mining project for improving web-based learning sites" , In Web Mining Workshop. Cataluna pp. 1-22, 2005.
[4] C. Romero, S. Ventura, E. García, "Data mining in course management systems: Moodle case study and tutorial" Computers & Education, Volume 51, Issue 1, August 2008, pp. 368-384.
[5] H.H. Hsu, C.H. Chen, and W.P. Tai, "Towards Error-Free and Personalized Web-Based Courses", In: The 17th International Conference on Advanced Information Networking and Applications, AINA-03. March 27-29, Xian,China, pp. 99-104, 2003.
[6] A. Kumar, "Rule-Based Adaptive Problem Generation in Programming Tutors and its Evaluation", In: The 12th International Conference on Artificial Intelligence in Education. July 18-22, Amsterdam, pp. 36- 44,2006.
[7] F. Berzal, J.C. Cubero, N. M. Sánchez, J.M. Serrano, and A. Vila, "Association rule evaluation for classification purposes" Actas del III Taller Nacional de Minería de Datos y Aprendizaje, TAMIDA2005, pp.135-144 ISBN: 84-9732-449-8 ,2005.
[8] W. West, B.R.S. Rosser, S. Monani, and L. Gurak, " How Learning Styles Impact ELearning:a Case Comparative Study Of Undergraduate Students Who Excelled, Passed Or Failed An Online Course", In Scientific/Technical Writing. E-learning, pp. 534-543, 2006.
[9] N. Kerdprasop, N. Muenrat, and K. Kerdprasop, "Decision Rule Induction in a Learning Content Management System" Proceedings of World Academy of Science, Engineering and Technology, pp.77-81, 2008.
[10] U. M. Fayyad, G. Pitatesky-Shapiro, P. Smyth, and R. Uthurasamy, "Advances in Knowledge Discovery and Data Mining", AAAI/MIT Press, 1996.
[11] W. Frawley, G. Piatetsky-Shapiro, and C. Matheus, "Knowledge Discovery in Databases: An Overview". AI Magazine, Fall 1992, pp. 213-228.
[12] R. Agrawal, T. Imielinski, and A.N. Swami, A. N., "Mining association rules between sets of items in large databases", In Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, pp. 207-216,1993.
[13] Z. Qiankun, "Association Rule Mining:A Survey, Technical Report",CAIS, Nanyang Technological University, Singapore , 2003
[14] R. Agrawal, and R. Srikant, "Fast algorithms for mining association rules", In Proc. 20th Int. Conf. Very Large Data Bases, VLDB, pp. 487- 499, 1994.
[15] http://www.cs.waikato.ac.nz/ml/weka/
[16] E. García, C. Romero, S. Ventura, C. Castro, and T. Calders, "Chapter 7: Association Rule Mining in Learning Management Systems." In: Hadebook of Educational Data Mining, Taylor&Francis Group, 2010.