Li, Ling Xiao and Abdul Rahman, Siti Soraya (2018) Students' learning style detection using tree augmented naive Bayes. Royal Society Open Science, 5 (7). p. 172108. ISSN 2054-5703, DOI https://doi.org/10.1098/rsos.172108.
Full text not available from this repository.Abstract
Students are characterized according to their own distinct learning styles. Discovering students' learning style is significant in the educational system in order to provide adaptivity. Past researches have proposed various approaches to detect the students' learning styles. Among all, the Bayesian network has emerged as a widely usedmethod to automatically detect students' learning styles. On the other hand, tree augmented naive Bayesian network has the ability to improve the naive Bayesian network in terms of better classification accuracy. In this paper, we evaluate the performance of the tree augmented naive Bayesian in automatically detecting students' learning style in the online learning environment. The experimental results are promising as the tree augmented naive Bayes network is shown to achieve higher detection accuracy when compared to the Bayesian network.
| Item Type: | Article |
|---|---|
| Funders: | UNSPECIFIED |
| Uncontrolled Keywords: | Automatic detection; Bayesian network; Learning styles |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| Divisions: | Faculty of Computer Science & Information Technology > Department of Artificial Intelligence |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 17 Jun 2019 06:34 |
| Last Modified: | 17 Jun 2019 06:34 |
| URI: | http://eprints.um.edu.my/id/eprint/21479 |
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