Detecting Learning Styles in Learning Management Systems Using Data Mining

Author:

Liyanage Madura Prabhani Pitigala1,Gunawardena K.S. Lasith1,Hirakawa Masahito1

Affiliation:

1. Interdisciplinary Graduate School of Science and Engineering, Shimane University

Publisher

Information Processing Society of Japan

Subject

General Computer Science

Reference38 articles.

1. [1] Szabo, M. and Flesher, K.: CMI Theory and Practice: Historical Roots of Learning Management Systems, Proc. World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education 2002 Chesapeake, VA: Association for the Advancement of Computing in Education (AACE'02), pp.929-936 (2002).

2. [2] Moodle Learning Management System (online), available from <http://www.moodle.org/> (accessed 2015-08-24).

3. [3] Graf, S. and List, B.: An evaluation of open source e-learning platforms stressing adaptation issues, Proc. Fifth IEEE International Conference on Advanced Learning Technologies (ICALT'05), pp.163-165 (2005).

4. [4] De Crook, M., Mofers, F., Veen, M.V., Rosmalen, P.V., Brouns, F., Boticario, J.G., Barrera, C., Santos, O., Ayala, A., Gaudioso, E., Hernández, F., Arana, C., Trueba, I., Fuentes, C. and Rodrigo, M.D.M.: Active learning for adaptive internet-state of the art, project deliverable report (online), available from <https://adenu.ia.uned.es/alfanet/reports/ALFANET_D12.pdf/> (accessed 2015-08-24).

5. [5] García, P., Amandi, A., Schiaffino, S.S. and Campo, M.: Evaluating Bayesian networks precision for detecting students' learning styles, Computers & Education, Vol.49, No.3, pp.794-808 (2007).

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