Web Intelligence, IEEE / WIC / ACM International Conference on (2005)
Compi?gne University of Technology, France
Sept. 19, 2005 to Sept. 22, 2005
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/WI.2005.125
Yen-Hung Kuo , National Cheng Kung University
Juei-Nan Chen , National Cheng Kung University
Yu-Lin Jeng , National Cheng Kung University
Yueh-Min Huang , National Cheng Kung University
Over the last years, we have witnessed an explosive growth of e-learning. More and more learning contents have been published and shared over the Internet. Therefore, how to progress an efficient learning process becomes a critical issue. This paper proposes a sequential mining algorithm to analyze learning behaviors for discovering frequent sequential patterns. By these patterns, we can provide suggestions for learners to select their interest learning contents. Different to other sequential mining algorithms, this study provides an incrementally method to analyze learning sequencing. More specifically, the mining algorithm in this paper can provide real-time analysis, and then report to learners for selecting learning contents more easily.
e-learning, data mining, sequential mining, frequent pattern, real-time analysis
Y. Kuo, Y. Jeng, Y. Huang and J. Chen, "Real-Time Learning Behavior Mining for e-Learning," Proceedings. The 2005 IEEE/WIC/ACM International Conference on Web Intelligence(WI), Compiegne, France, 2005, pp. 653-656.