Extended real-time learning behavior mining

Yen Hung Kuo, Yueh Min Huang, Juei Nan Chen, Yu Lin Jeng

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationProceedings - 5th IEEE International Conference on Advanced Learning Technologies, ICALT 2005
Pages440-441
Number of pages2
DOIs
Publication statusPublished - 2005 Dec 1
Event5th IEEE International Conference on Advanced Learning Technologies, ICALT 2005 - Kaohsiung, Taiwan
Duration: 2005 Jul 52005 Jul 8

Publication series

NameProceedings - 5th IEEE International Conference on Advanced Learning Technologies, ICALT 2005
Volume2005

Other

Other5th IEEE International Conference on Advanced Learning Technologies, ICALT 2005
CountryTaiwan
CityKaohsiung
Period05-07-0505-07-08

All Science Journal Classification (ASJC) codes

  • Engineering(all)

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