Extended real-time learning behavior mining
Yen‐Hung Kuo, Yueh‐Min Huang, Juei-Nan Chen, Yu-Lin Jeng · 2005
Based on our previous work (Y. H. Kuo et al., 1999), learning patterns can be discovered and recommended to learners. This paper extends the proposed problem to handle the questionable mining results. According to the learning patterns discovered by using learning histories, it happened whenever the learners have ineffective learning behaviors, and we define them as questionable mining results. These ineffective behaviors may induce the bias suggestions. Therefore, we propose a candidate sequence set generation process to take care the stumble learning behavior.