Abstract

Early identification of students at risk of academic failure is important for intervention strategies in higher education. This study predicts end-of-term success using 11,400 student records from a public university between 2018 and 2023. Learning management system interactions, attendance and demographic variables were considered together. The XGBoost model identified at-risk students with 84% sensitivity by the fourth week of the term. SHAP values were used to provide advisors with understandable, student-level explanations.

Keywords:educational data miningstudent performanceearly warningSHAPexplainability

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Ethics Approval
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How to Cite

Arslan, C., Şahin, M., & Öztürk, S. (2025). Predicting Student Performance in Higher Education and an Explainable Early Warning System with SHAP. International Journal of Science and Technology Research, 7(2), 19–34. https://doi.org/10.99999/ubtad.2025.12
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CC BY 4.0

© 2025 Cem Arslan, Merve Şahin, Selin Öztürk. This article is distributed under the terms of the CC BY 4.0 license, which permits unrestricted use, distribution and reproduction in any medium, provided the original work is properly cited. License text