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Mining Educational Data to Analyze the Student Motivation Behavior

Authors: Kunyanuth Kularbphettong, Cholticha Tongsiri


The purpose of this research aims to discover the knowledge for analysis student motivation behavior on e-Learning based on Data Mining Techniques, in case of the Information Technology for Communication and Learning Course at Suan Sunandha Rajabhat University. The data mining techniques was applied in this research including association rules, classification techniques. The results showed that using data mining technique can indicate the important variables that influence the student motivation behavior on e-Learning.

Keywords: association rule mining, classification techniques, e- Learning, Moodle log Motivation Behavior

Digital Object Identifier (DOI):

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[1] 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.
[2] 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.
[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] Waiyamai,K "Improving Quality Graduate Student by Data Mining".Departement of Computer engineering. Faculty of Engineering. Kasetsart University, Bangkok Thailand. 2003.
[6] B. Minaei-Bidgoli, D. A. Kashy, G. Kortemeyer and, W. F. Punch."Predicting student performance: an application of data mining methods with the educational web-based system LON-CAPA" In Proceedings of ASEE/IEEE Frontiers in Education Conference, Boulder, CO: IEEE, 2003.
[7] 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.
[8] 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.
[9] 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.
[10] 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.
[11] 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.
[12] U. M. Fayyad, G. Pitatesky-Shapiro, P. Smyth, and R. Uthurasamy, "Advances in Knowledge Discovery and Data Mining", AAAI/MIT Press, 1996.
[13] W. Frawley, G. Piatetsky-Shapiro, and C. Matheus, "Knowledge Discovery in Databases: An Overview". AI Magazine, Fall 1992, pp. 213-228.
[14] 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.
[15] Z. Qiankun, "Association Rule Mining:A Survey, Technical Report",CAIS, Nanyang Technological University, Singapore , 2003
[16] 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.
[17] H. Edelstein, "Introduction to Data Mining and Knowledge Discovery", Third Edition. Two Crows Corporation, Potomac, MD, USA, 1999.
[18] Parr Rud, O. "Data Mining Cookbook. Modeling Data for Marketing, Risk, and Customer Relationship Management". John Wiley & Sons, Inc.; 2001.
[19] S. Mitra, and T. Acharya., "Data Mining. Multimedia, Soft Computing, and Bioinformatics". John Wiley & Sons, Inc. Hoboken, New Jersey; 2003.
[20] J. R. Quinlan, "Introduction of decision tree", Journal of Machine learning", pp. 81-106, 1986.
[21] L. Hyafil, and RL. Rivest, "Constructing Optimal Binary Decision Trees is NP-complete" , Information Processing Letters, Vol. 5, No. 1. (1976), pp. 15-1 R. Kohavi and J. R. Quinlan. Decision-tree discovery.
[22] A. Kumar Sharma and S. Suruchi, "A Comparative Study of Classification Algorithms for Spam Email Data Analysis", International Journal on Computer Science and Engineering (IJCSE), Vol. 3 No. 5, pp. 1891-1895. May 2011
[24] 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.