A Hybrid Ensemble Learning Framework for Educational data mining and Learning Analytics Research from an Interdisciplinary Perspective

Abstract

The growing volume of educational data generated by Virtual Learning Environments (VLEs) presents unprecedented opportunities for data-driven student support. This paper proposes HELM a Hybrid Ensemble Learning framework with Multi-modal behavioral analytics designed to predict student academic performance and provide early at-risk identification using the Open University Learning Analytics Dataset (OULAD). HELM integrates five interdisciplinary components: (1) multi-source temporal feature engineering from VLE interaction logs, assessment records, and registration data; (2) unsupervised behavioral clustering to identify four distinct student archetypes; (3) a SMOTE-balanced stacking ensemble combining Random Forest, XGBoost, LightGBM, and Gradient Boosting with a Logistic Regression meta-learner; (4) an Early Warning System (EWS) evaluated across nine temporal prediction windows; and (5) SHAP-based explainability analysis. Experimental results on 32,593 student records demonstrate that HELM achieves an AUC-ROC of 0.9412, accuracy of 0.8876, and F1-score of 0.8901, outperforming all individual baseline models. The EWS achieves AUC > 0.85 as early as Day 30 of the module, enabling timely institutional intervention. Interdisciplinary analysis further reveals that socioeconomic deprivation (IMD band), prior education level, and VLE engagement regularity are the strongest predictors of student success. These findings offer actionable insights for educators, policymakers, and learning system designers.