Data mining the factors of E-learning performance through decision trees and apriori associated rules

Journal of Internet Technology

Volumn 9, Issue 4, 2008, Pages 411-419

  Hou, Tung Hsu ^a   Houa, Hsing Yu ^b  

^a NATIONAL YUNLIN UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Taiwan)

^b TRANSWORLD INSTITUTE OF TECHNOLOGY   (Taiwan)

Author keywords

Data mining; E learning; Learning performance

Indexed keywords

DATA MINING; DECISION SUPPORT SYSTEMS; DECISION TABLES; DECISION THEORY; DECISION TREES; E-LEARNING; INFORMATION MANAGEMENT; INTERNET; KNOWLEDGE MANAGEMENT; LEARNING SYSTEMS; MATHEMATICAL MODELS; MINING; MULTIMEDIA SYSTEMS; STATISTICAL METHODS; TEACHING; TREES (MATHEMATICS);

APRIORI; CLEMENTINE; COMPUTER EXPERIENCES; CREATIVE PERFORMANCES; CROSS TABLES; DEPENDENT VARIABLES; E - LEARNINGS; EDUCATION COURSES; INTERACTIVE LEARNINGS; LEARNING EXPERIENCES; LEARNING PERFORMANCE; LEARNING STYLES; LOGICAL PERFORMANCES; STATISTICAL ANALYSIS; WEB LEARNINGS;

EDUCATION;

EID: 55349141105     PISSN: 16079264     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Conference Paper

References (27)

1. Agrawal, R, and Srikant, R , Fast Algorithms for Mining Association Rules in Large Databases, Proceedings of the 20th International Conference on Very Large Data Based, 1994, pp.478-499.
2. Ali, R. M., Kor, L. W, Association between Brain Hemisphericity, Learning Styles and Confidence in Using Graphics Calculator for Mathematics, Eurasia J. Math., Sci. & Tech. Ed., Vol. 3, No.2, 2007, pp. 127-131.
3. American Society for Training and Development, from http://www.astd.com, 2001.
4. Chen, C. M., Hsieh, Y. L., Hsu, S. H., Mining learner profile utilizing association rule for web-based learning diagnosis, Expert Systems With Applications Vol. 33, Issue 1, July, 2007, pp. 6-22.
5. Chiang, T. Y., A Study of Attitude and Literacy Toward Computer and Relationships about Junior Secondary and Primary School Students, National Cheng Chi University unpublished Ph. D Dissertations, 1996.
6. Feng, T. & Fionn, M., Towards Knowledge Discovery from WWW Log Data, Proceedings of the International Conference on Information Technology: Coding and Computing, 2000.
7. Hamdi, M. S., MASACAD: A multi-agent approach to information customization for the purpose of academic advising of students, Applied Soft Computing Journal, Vol. 7, Issue 3, June, 2007, pp. 746-771.
8. Hawkins, R., Computer Literacy and Computer Use Among College Students: Differences in Black and White, The Journal of Negro Education, Vol. 66, No. 2, 1997, pp. 147-158.
9. Hou,T.H, and Hou, H.Y., A learner's perspective on the factors of E-Learning performance, Journal of Internet Technology, Vol. 9, No. 2,2008, pp. 179-184.
10. Kolb, D. A., Learning Style technical manual, Boston: Mcber and Company, (1976).
11. Lee, J., Hong, N. L. and Ling, N. L., An analysis of students' preparation for the virtual learning environment, The internet and higher education, 2002, pp.231-242.
12. McCarthy, B., The 4Mat system: Teaching to Learning Styles with Right/left Mode Techniques, EXCEL. Inc. 1987.
13. Michael J.A. and Gordon S. L., data mining techniques: for marketing, sales, and customer support. New York: John Wiley and Sons, 1997.
14. Nick van Dam, The e-Learning Fieldbook, New York: McGraw-Hill, 2003.
15. Picciano, A. G., Distance learning: making connections across virtual space and time, Prentice-Hall, Inc, 2001.
16. Piccoli, G, Ahmad, R., and Ives, B., Web-based virtual learning environments: A research Framework and a Preliminary Assessment of Effectiveness in Basic IT Skills Training, MIS Quarterly, Vol. 25, No.4, 2001, pp.401-426.
17. Quarstein V. A., and Peterson, P. A., Assessment of Cooperative Learning: A Goal-Criterion Approach, Innovative Higher Education,Vol. 26, No. 1, 2001, pp.59-77.
18. Quinlan, J. R., C4.5: Programs for Machine Learning. San Mateo, California: Morgan Kaufmann, 1993.
19. Quinlan, J. R., Induction of Decision Trees, Machine Learning, Vol. 1, No. 1, 1986, pp.81-106.
20. Rosenberg, M.J., E-learning: strategies for delivering knowledge in the digitalage, New York : Mcgraw Hill, 2001.
21. Rasmussen, K. L., and Davidson-Shivers, G. V., Hypermedia and learning Styles: Can performance be influenced?, Journal of Education Multimedia and Hypermedia, Vol. 7, No.4, 1998, pp. 291-308.
22. Shannon, C. E. and W. Weaver, Mathematical Theory of Communication, University of Illinois Press, IL, 1949.
23. Shores, E. F., and Grace C., The Portfolio Book A Step-by-Step Guide for Teachers, Gryphon House, Inc, (1998).
24. Soloman, B. A., and Felder, R. M., Index of Learning Styles Questionnaire, from http://www.engr.ncsu.edu/learningstyles/ilsweb.html, 2005.
25. Stephen, W. H., and Marshall, G. J., The Five Levels of Web Use in Education: Factors to Consider in Planning Online Course, Educational Technology, 1999, pp.28-32.
26. Sun, P. C., Cheng, H. K., Lin, T. C., Wang, F. S., A design to promote group learning in e-learning: Experiences from the field, Computers & Education Vol. 50, Issue 3, April, 2008, pp. 661-677.
27. Wang, T. I., Wang, K, T., Huang, Y. M., Using a style-based ant colony system for adaptive learning, Expert Systems With Applications, Vol. 34, Issue 4, May, 2008, pp. 2449-2464.