Publication: Enhancing knowledge integration from multiple experts to guiding personalized learning paths for testing and diagnostic systems
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Issued Date
2021-01-01
Resource Type
ISSN
2666920X
Other identifier(s)
2-s2.0-85122308151
Rights
Mahidol University
Rights Holder(s)
SCOPUS
Bibliographic Citation
Computers and Education: Artificial Intelligence. Vol.2, (2021)
Suggested Citation
Dechawut Wanichsan, Patcharin Panjaburee, Sasithorn Chookaew Enhancing knowledge integration from multiple experts to guiding personalized learning paths for testing and diagnostic systems. Computers and Education: Artificial Intelligence. Vol.2, (2021). doi:10.1016/j.caeai.2021.100013 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/76723
Research Projects
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Thesis
Title
Enhancing knowledge integration from multiple experts to guiding personalized learning paths for testing and diagnostic systems
Abstract
The testing and diagnostic systems have been considered to be useful for students because they can point to their learning problems to provide helpful suggestions for improving the knowledge. The previous approach shows that using the knowledge from multiple experts can develop a testing and diagnostic system that provides more accurate suggestions for learners comparing to the system using a single expert. Nevertheless, the low-quality knowledge integration method of the multi-expert approach can provide some inaccurate learning suggestions. This work proposes a practical method for enhancing knowledge integration from multiple experts to provide more effective learning suggestions. An experiment has been conducted on freshmen in university to evaluate the effectiveness of the proposed method. The results show that the proposed approach not only improves the learning achievements of the students but also decreases the number of reconsidering cases.