Educational Data Mining for Student Performance Prediction: A Systematic Literature Review (2015-2021)
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...
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| Veröffentlicht in: | International journal of emerging technologies in learning 2022-01, Vol.17 (5), p.147-179 |
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| container_title | International journal of emerging technologies in learning |
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| creator | Bin Roslan, Muhammad Haziq Chen, Chwen Jen |
| description | 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. |
| doi_str_mv | 10.3991/ijet.v17i05.27685 |
| format | Article |
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| fulltext | fulltext |
| identifier | ISSN: 1863-0383 |
| ispartof | International journal of emerging technologies in learning, 2022-01, Vol.17 (5), p.147-179 |
| issn | 1863-0383 1863-0383 |
| language | eng |
| recordid | cdi_proquest_journals_2666913434 |
| source | EZB-FREE-00999 freely available EZB journals; EBSCOhost Education Source |
| subjects | Data mining Literature reviews Systematic review |
| title | Educational Data Mining for Student Performance Prediction: A Systematic Literature Review (2015-2021) |
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