Muhammad Haziq, Hassan and Chen, Chwen Jen (2022) Educational Data Mining for Student Performance Prediction : A Systematic Literature Review (2015-2021). International Journal of Emerging Technologies in Learning, 17 (5). pp. 147-179. ISSN 1868-8799
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Abstract
—This systematic literature review aims to identify the recent research trend, most studied factors, and methods used to predict student academic performance from 2015 to 2021. The PRISMA framework guides the study. The study reviews 58 out of 219 research articles from Lens and Scopus databases. The findings indicate that the research focus of current studies revolves around identifying factors influencing student performance, data mining (DM) algorithms performance, and DM related to e-Learning systems. It also reveals that student academic records and demographics are primary aspects that affect student performance. The most used DM approach is classification and the Decision Tree classifier is the most employed DM algorithm.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | Educational Data Mining (EDM), data mining (DM) techniques, prediction studies, student academic performance, systematic literature review. |
| Subjects: | L Education > L Education (General) P Language and Literature > PN Literature (General) |
| Divisions: | Academic Faculties, Institutes and Centres > Faculty of Cognitive Sciences and Human Development Faculties, Institutes, Centres > Faculty of Cognitive Sciences and Human Development Academic Faculties, Institutes and Centres > Faculty of Cognitive Sciences and Human Development |
| Depositing User: | Gani |
| Date Deposited: | 21 Apr 2022 02:31 |
| Last Modified: | 21 Apr 2022 02:31 |
| URI: | http://ir.unimas.my/id/eprint/38366 |
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