Real-Time Learning Behavior Mining for e-Learning

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.

• Type: Conference
• Year: 2005
• Where: WEBI
• Authors: Yen-Hung Kuo, Juei-Nan Chen, Yu-Lin Jeng, Yueh-Min Huang
• Keywords: Efficient Learning Process, Internet Technology, Learning Contents, Sequential Mining Algorithms, WEBI 2005