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Fifth IEEE International Conference on Advanced Learning Technologies (ICALT'05)
Kaohsiung, Taiwan
July 05-July 08
ISBN: 0-7695-2338-2
Yen-Hung Kuo, National Cheng Kung University
Yueh-Min Huang, National Cheng Kung University
Juei-Nan Chen, National Cheng Kung University
Yu-Lin Jeng, National Cheng Kung University
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
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