A Generalized E-Learning Usage Behaviour Model by Data Mining Technique

Authors

  • Songsakda Chayanukro Suan Dusit Rajabhat University, Thailand
  • Massudi Mahmuddin School of Computing, Universiti Utara Malaysia, Malaysia
  • Husniza Husni School of Computing, Universiti Utara Malaysia, Malaysia

DOI:

https://doi.org/10.32890/jict2014.13.3

Keywords:

e-Learning, user behaviour, data mining

Abstract

Current study on e-Learning user’s behaviour model obtained the specific models. In many cases, the e-Learning user’s behaviour model for open source e-Learning system such as Moodle, which can predict learning outcome or learning performance is still defi cient and cannot generally apply in many institutions due to the fact that the majority of prediction models were developed particularly for certain institutions. This study proposes to produce a general model that can make a prediction of learning outcome inspired by Skinner’s theory, which explains the relationship between learner, achievement, and learner reinforcement. This study proposes similar patterns in e-Learning user’s behaviour models of different institutions by the data-mining technique based on the learning environment theory. Therefore, this research is conducted in three main phases; include data preparation from weblog of different institutions with the same e-Learning system, data extraction by the accurate classifi er model fi nding process and model verification for generating a verification pattern. The research outcome will be a similar pattern that could be used as a direction for creating a more appropriate e-Learning users’ behaviour model and could be used broadly in other higher institutions.

 

References

Chance, P. (2003). The study of learning and behaviour learning & behaviour (5th ed.). Belmont, CA: Phoenix Color Corp.

Chien Ming, C., Chao Yi, L., Te Yi, C., Bin Shyan, J., & Tsong Wuu, L. (2007). Diagnosis of students’ online learning portfolios. Paper presented at the Frontiers In Education Conference - Global Engineering: Knowledge Without Borders, Opportunities Without Passports, FIE ‘07, 37th Annual, 10−13 Oct.

Chun Xia, Q., Hui Bao, C., Chang Yi, L., & Yue Xing, S. (2010). Design an active e-learning system. Paper presented at the 2nd International Conference on Education Technology and Computer (ICETC), 22−24 June.

Cotton, J. (1995). The theory of learning. London: Biddles, Guildford and King’s Lynn.

Dai, S., & Zhang, P. (2008). A data mining algorithm in distance learning. Paper presented at the CSCWD 2008. 12th International Conference on Computer Supported Cooperative Work in Design, 16-18 April.

Graf, S., & List, B. (2005). An evaluation of open source e-learning platforms stressing adaptation issues. Paper presented at the ICALT 2005. Fifth IEEE International Conference on Advanced Learning Technologies, 5−8 July.

Haythornthwaite, C., & Andrews, R. (2011). E-learning theory and practice. Singapore 048763: Sage Publications Asia-Pacific.

Henry, G. T. (1990). Sample selection approaches practical sampling, 21,17−32. California: Sage Publications.

Hsien Tang, L., Chih Hua, W., Chia Feng, L., & Shyan Ming, Y. (2009). Annotating learning materials on Moodle LMS. Paper presented at the ICCTD 09. International Conference on Computer Technology and Development, 13−15 Nov. http://jict.uum.edu.my/ Journal of ICT, 13, 2014, pp: 37–

Kebin, H., Feimin, L., Ming, Z., Feng, W., & Xiaoshuang, X. (2008). Design and implement on e-learning behaviour mine system. Paper presented at the WGEC 08. Second International Conference on Genetic and Evolutionary Computing, 25−26 Sept.

Levy, S. (2003). Six factors to consider when planning online distance learning programs in higher education. Online Journal of Distance Learning Administration, 6 (1).

Lingyan, W., Jian, L., Lulu, D., & Pengkun, L. (2010). E-Learning evaluation system based on data mining. Paper presented at the 2nd International Symposium on Information Engineering and Electronic Commerce (IEEC), 23−25 July.

Lipo, W. (2005). Support vector machines: Theory and applications, 177. Berlin: Springer.

Min, J. (2005). Development of an e-learning system for teaching machining technology. Paper presented at the Proceedings of the 2005 International Conference on Active Media Technology, 19−21 May.

Moodle.org. (2011). About moodle. Retrieved from http://docs.moodle.org

O’Donohue, W., & Ferguson, K. E. (2001). The psychology of B. F. Skinner. United States of America: Sage Publication.

Rashty, D. (1999). Traditional learning vs. eLearning. Retrieved from http://www.rashty.com

Ribeiro, B., & Cardoso, A. (2008). Evaluation system for e-learning with pattern mining tools. Paper presented at the SMC 2008. IEEE International Conference on Systems, Man and Cybernetics 12−15 Oct.

Romero, C. B., Ventura, S., & Garcia, E. (2008). Data mining in course management systems: Moodle case study and tutorial. Computers & Education, 51(1), 368−384. Ruiz Reyes, N., Vera Candeas, P., Galan, S. G., Viciana, R., Canadas, F., &

Reche, P. J. (2009, 22−24 June). Comparing open-source e-learning platforms from adaptivity point of view. Paper presented at the EAEEIE Annual Conference. Sancristobal, E., Castro, M., Harward, J., Baley, P., DeLong, K., & Hardison,

J. (2010). Integration view of web labs and learning management systems. Paper presented at the Education Engineering (EDUCON), IEEE, 14−16 April. scorm.com. (2012). Shareable content object reference model (SCORM). Retrieved from http://scorm.com

Shiba, O. A. A., Sulaiman, M. N., Ahmad, F., & Mamat, A. (2003). An experimental evaluation of case slicing as a new classification technique. Journal of Information and Communication Technology, 2, 105−117.

Vapnik, V., & Cortes, C. (1995). Support-vector networks. Machine Learning, 20, 273−279. http://jict.uum.edu.my/ Journal of ICT, 13, 2014, pp: 37–

Wen Hai, G. (2010, 11−14 July). Research on client behaviour pattern recognition system based on Weblog mining. Paper presented at the

ICMLC 2010 International Conference on Machine Learning and Cybernetics.

Witten, I. H., & Frake, E. (2005). Data mining: Practical machine learning tools and techniques (2nd ed.). San Francisco, CA: Morgan Kaufmann.

Yilmaz, Y. (2010). Higher education institutions in Thailand and Malaysia: Can they deliver? Fiscal and Legislative Affairs Office of Revenue Analysis, DC.

Zaki, N. M., Deris, S., & Chin, K. K. (2002). Extending the decomposition algorithm for support vector machines training. Journal of Information and Communication Technology, 1, 17−19.

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Published

19-02-2014

Issue

Vol. 13 (2014): Journal of Information and Communication Technology (JICT) Vol. 13, 2014

Section

Articles

How to Cite

Chayanukro, S., Mahmuddin, M., & Husni, H. (2014). A Generalized E-Learning Usage Behaviour Model by Data Mining Technique. Journal of Information and Communication Technology, 13, 37-53. https://doi.org/10.32890/jict2014.13.3

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Identifiers DOI 10.32890/jict2014.13.3

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