Educational Data Mining for Student Performance Prediction: A Systematic Literature Review (2015-2021)

Authors

  • Muhammad Haziq Bin Roslan University Malaysia Sarawak, Jln Datuk Mohammad Musa, 94300 Kota Samarahan, Sarawak, Malaysia
  • Chwen Jen Chen University Malaysia Sarawak,Jln Datuk Mohammad Musa, 94300 Kota Samarahan, Sarawak, Malaysia

DOI:

https://doi.org/10.3991/ijet.v17i05.27685

Keywords:

Educational Data Mining (EDM), data mining (DM) techniques, prediction studies, student academic performance, systematic literature review

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.

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Published

2022-03-14

How to Cite

Bin Roslan, M. H., & Chen, C. J. (2022). Educational Data Mining for Student Performance Prediction: A Systematic Literature Review (2015-2021). International Journal of Emerging Technologies in Learning (iJET), 17(05), pp. 147–179. https://doi.org/10.3991/ijet.v17i05.27685

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Section

Papers