文章基本信息

标题：Educational Data Mining for Student Performance Prediction: A Systematic Literature Review (2015-2021)
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作者：Muhammad Haziq Bin Roslan ; Chwen Jen Chen
期刊名称：International Journal of Emerging Technologies in Learning (iJET)
印刷版ISSN：1863-0383
出版年度：2022
卷号：17
期号：5
DOI：10.3991/ijet.v17i05.27685
语种：English
出版社：Kassel University Press
摘要：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.