Volume 4 • Issue 1 • PP: 33–41 • 2025
Machine Learning for At-Risk Student Identification in Virtual Learning Environments: A Multi-Classifier Analysis Using the Open University Learning Analytics Dataset
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© 2025 The Author(s). Published by ASPG. This article is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
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The detection of students who may face academic difficulties or withdraw early gives universities a limited window in which to provide effective support. This study evaluates four machine-learning classifiers on the Open University Learning Analytics Dataset (OULAD), which contains data from 32,593 students across 22 distance-learning courses. The classifiers—logistic regression, decision tree, random forest, and gradient boosting—use demographic and contextual variables together with virtual learning environment (VLE) engagement features. VLE behaviour dominates Random Forest feature importance: total clicks, active VLE days, average daily clicks, and first-assessment score account for 92.8% of total importance. Random Forest achieves strong held-out test performance (AUC = 0.998, F1 = 0.978, accuracy = 98.2%), while the Decision Tree achieves an AUC of 0.959, illustrating the performance–interpretability trade-off. At-risk students generate an average of 49.0 VLE clicks, compared with 203.0 for not-at-risk students (t = 104.0, p < 0.001). The study describes an end-to-end prediction pipeline and discusses its implications for early-alert systems and the ethical deployment of predictive analytics in higher education.
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References
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