Volume 5 • Issue 1 • PP: 28–36 • 2026
Early Identification of At-Risk Students in Virtual Learning Environments Using Ensemble Machine Learning and Behavioural Analytics
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The academic success of students who are nearing academic failure should be Identifying students who are at risk of academic failure or course withdrawal at an early stage of their enrolment remains one of the most pressing challenges in higher and distance education. The research assesses the performance of seven machine learning classifiers which include Logistic Regression Decision Tree Random Forest Gradient Boosting Decision Tree (GBDT) AdaBoost Naive Bayes and Multilayer Perceptron for predicting student risk at an early stage based on a behavioural and demographic dataset derived from the Open University Learning Analytics Dataset (OULAD). The dataset contains 7895 student records which represent a single module and show eight demographic factors together with eight Virtual Learning Environment (VLE) usage patterns. All classifiers were evaluated through five-fold stratified cross-validation. The GBDT model achieved the best results with an AUC-ROC value of 0.782 (} 0.003) and an accuracy rate of 0.708 (} 0.005) which produced an F1 score of 0.729 (} 0.006) and a recall rate of 0.769 (} 0.006). The analysis of feature importance showed that late sub-mission count (I = 0.304) and total VLE clicks (I = 0.150) together with first assessment score (I = 0.135) serve as the three most valuable predictive indicators because they help identify student engagement patterns which become evident through VLE traces that educational institutions collect from students during their first module. Educational institutions can utilize learning management system data to implement effective combi-nation methods which enable them to execute necessary teaching methods even though they do not need to gather additional expense data. The article presents design elements which both create early warning systems and manage the ethical use of predictive analytics within educational systems.
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References
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