Predicting students' academic performance has become a key area in Educational Data Mining and allows for the
early detection of at-risk students by higher education institutions; this provides educators much-needed information to make
decisions about when to offer critical interventions. The ability to identify students experiencing academic challenges or who
may be withdrawing from a program in a timely manner allows for there to be more focused support strategies put in place to
address the students' needs before the intervention timelines have passed. The rapid growth of this research area is due to the
combination of widely available educational datasets and advances in machine learning research. However, there is a wide
variety of algorithmic approaches, methods of gathering data, and evaluation frameworks cited throughout all of the current
literature. Therefore, while this review provides an aggregated summary of the current literature on accuracy, there is no
consideration given to whether or not predictions made using machine learning may be effectively utilized to improve student
academic performance which represents an important gap in the literature for institutions wanting to implement.
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Using Machine Learning Techniques. Education Sciences, 11(9), 552. https://doi.org/10.3390/educsci11090552
[2] Al-Ahmad, B. I., Alzaqebah, A., Alkhawaldeh, R., Al-Zoubi, A. M., Lo, H., & Ali, A. (2025). Predicting academic
performance for students’ university: case study from Saint Cloud State University. PeerJ Computer Science, 25(8),
e3087. https://doi.org/10.7717/peerj-cs.3087
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[4] Vaarma, M., et al. (2024). Predicting student dropouts with machine learning. Evaluation and Program Planning, 104,
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[7] Goren, O., et al. (2024). Early Prediction of Student Dropout in Higher Education. Proceedings of the 17th
International Conference on Educational Data Mining, 32. DOI/Link to be added
[8] Romero, S., et al. (2025). Statistical and machine learning models for predicting dropout. PLoS One, 20(4), e0325047.
https://doi.org/10.1371/journal.pone.0325047
[9] Ahmed, E., et al. (2024). Student Performance Prediction Using Machine Learning. Journal of Electrical Engineering
and Medical Informatics, 7(1), 4067721. DOI/Link to be added
[10] Wang, J., et al. (2025). Machine learning approach to student performance prediction based on online learning. PLoS
One, 20(2), e0299018. https://doi.org/10.1371/journal.pone.0299018
[11] Al-Tameemi, G., et al. (2024). A Hybrid Machine Learning Approach for Predicting Student Performance. Procedia
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statement. BMJ, 372, n71. https://doi.org/10.1136/bmj.n71
[13] Wolff, R. F., et al. (2019). PROBAST: A Tool to Assess Risk of Bias and Applicability of Prediction Model Studies.
Annals of Internal Medicine, 170(1), 51–58. https://doi.org/10.7326/M18-1376
[14] Moons, K. G. M., et al. (2015). Transparent Reporting of a multivariable prediction model for Individual Prognosis Or
Diagnosis (TRIPOD): Explanation and Elaboration. Annals of Internal Medicine, 162(1), W1–W73.
https://doi.org/10.7326/M14-0699
