Fifth IEEE International Conference on Advanced Learning Technologies (ICALT'05) Kaohsiung, Taiwan July 05-July 08 ISBN: 0-7695-2338-2
Based on our previous work [3], learning patterns can be discovered and recommend to the learners. This paper extends the proposed problem to handle the questionable mining results. According to the learning patterns are discovered by using learning histories. It may be 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.
Index Terms:
data mining, stumble learning pattern
Citation:
Yen-Hung Kuo, Yueh-Min Huang, Juei-Nan Chen, Yu-Lin Jeng, "Extended Real-Time Learning Behavior Mining," icalt, pp.440-441, Fifth IEEE International Conference on Advanced Learning Technologies (ICALT'05), 2005 Usage of this product signifies your acceptance of the Terms of Use. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||