Mining Educational Data to Support Students’ Major Selection

Authors: Kunyanuth Kularbphettong, Cholticha Tongsiri

Abstract:

This paper aims to create the model for student in choosing an emphasized track of student majoring in computer science at Suan Sunandha Rajabhat University. The objective of this research is to develop the suggested system using data mining technique to analyze knowledge and conduct decision rules. Such relationships can be used to demonstrate the reasonableness of student choosing a track as well as to support his/her decision and the system is verified by experts in the field. The sampling is from student of computer science based on the system and the questionnaire to see the satisfaction. The system result is found to be satisfactory by both experts and student as well. 

Keywords: Data mining technique, the decision support system, knowledge and decision rules.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1336368

References:

[1] 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.
[2] 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.
[3] K. Kularbphettong, and C.Tongsiri."Student Motivation Behavior on e-Learning based on Data Mining Techniques”.Proceeding of International Conference on Data Analysis and Decision Making. Prague, Czech Republic July 08-09, 2013.
[4] K. Kularbphettong, and C. Tongsiri. "Mining Educational Data to Analyze the Student Motivation Behavior” .Proceeding of International Conference on Information Technology and Computer Science. Paris, France Augest 22-23, 2012.
[5] K. Kularbphettong, C. Tongsiri and P. Waraporn. "Analysis of Student Motivation Behavior on e-Learning Based on Association rule mining”.Proceeding of International Conference on Education and Information Technology. Paris, France July 27-28, 2012.
[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] U. M. Fayyad, G. Pitatesky-Shapiro, P. Smyth, and R. Uthurasamy, "Advances in Knowledge Discovery and Data Mining”, AAAI/MIT Press, 1996.
[8] W. Frawley, G. Piatetsky-Shapiro, and C. Matheus, "Knowledge Discovery in Databases: An Overview”. AI Magazine, Fall 1992, pp. 213-228.
[9] H. Edelstein, "Introduction to Data Mining and Knowledge Discovery”, Third Edition. Two Crows Corporation, Potomac, MD, USA, 1999.
[10] Parr Rud, O. "Data Mining Cookbook. Modeling Data for Marketing, Risk, and Customer Relationship Management”. John Wiley & Sons, Inc.; 2001.
[11] 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.
[12] Kevin B.Korb and Ann E. Nicholson. "Introduction to Bayesian Networks”, Bayesian Artificial Intelligence. 2010, pp 29-54. CRC Press.
[13] M. Singh and M. Valtorta. Construction of Bayesian Network Structures from Data: a Brief Survey and an efficient Algorithm. International Journal of Approximate Reasoning, 1995, volume 12, pages 111-131, Elsevier Science Inc.
[14] WEKA Source: http://www.cs.waikato.ac.nz/ml/weka/