Abstract
This paper presents a Rough Set approach to analyze of students academic performance in a Web-based learning support system (WLSS). Web-based education has become a very important area of educational technology. This paper considers individual learners working alone without support from a teacher to provide guidance and advice on learning approach. Learners may have access to a wealth of material but may be faced with other problems such as material selection, planning a learning strategy, maintaining motivation and sequencing learning sub-goals. It might create a situation where some students may not be able to improve their grade as well as they could, compared to a face-to-face course. What if customized course materials were prepared for each student? It might fill this gap. Their records, such as grades for prerequisite courses or some personal factors that seems to affect their academic performance are used as student profile. In this paper, we discuss how to use Rough Sets to analyze student personal information to assist students with effective learning.
Decision rules are obtained using Rough Set based learning to predict academic performance, and it is used to determine a path for course delivery.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
WebCT, WebCT home page, http://www.webct.com/.
Brusilovsky, P. Knowledge Tree: A Distributed Architecture or Adaptive E-learning. WWW 2004, May 17–24, 2004, ACM 104–111.
Zaiane, O. R., “Building a Recommender Agent for e-Learning Systems”, International Conference on Computers in Education (ICCE’02), Auckland, December 03–06 pp 55–59.
Fan, L., “Personalization and Adaptation in Web-based Learning Support System” Proceedings of the Web-based Support Systems (WSS04), Beijing, China, 2004.
Brusilovsky, P., Anderson, J., “An adaptive System for Learning Cognitive Psychology on the Web”, World Conference of the WWW, Orlando, November 7–12, 1998, pp 92–97.
Liang, A.H., Ziarko, W., and Maguire, B., “The Application of a Distance Learning Algorithm in Web-Based Course Delivery.” Rough Sets and Current Trends in Computing 2000: 338–345, Banff, Canada.
Magagula, C.M., Ngwenya, A.P., “A Comparative Analysis of the Academic Performance of Distance and On-campus Learners.” Turkish Online Journal of Distance Education, October 2004, Volume: 5 Number: 4
Lavin, D., “The Prediction of Academic Performance: A Theoretical Analysis and Review of Research.” New York: Russell Sage Foundation, 1965, pp. 182.
Vasilakos, T., Devedzic, V., Kinshuk, Pedrycz, W., “Computational Intelligence in Web-Based Education: A Tutorial”, JILR, Vol. 15, No.4, 2004, pp. 299–318.
Pawlak, Z., “Rough Sets”, Kluwer Academic Publishers, 1991.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2007 Springer-Verlag London Limited
About this paper
Cite this paper
Fan, L., Matsuyama, T. (2007). Rough Set Approach to Analysis of Students Academic Performance in Web-based Learning Support System. In: Akhgar, B. (eds) ICCS 2007. Springer, London. https://doi.org/10.1007/978-1-84628-992-7_16
Download citation
DOI: https://doi.org/10.1007/978-1-84628-992-7_16
Publisher Name: Springer, London
Print ISBN: 978-1-84628-990-3
Online ISBN: 978-1-84628-992-7
eBook Packages: Computer ScienceComputer Science (R0)