EDuHINet: An Educational Heterogeneous Information Network for Online Course Performance Prediction

Le résumé fourni par la source

Accurate online course performance prediction is important for timely academic support, yet existing studies often rely on either static learner attributes or temporal behavioral data alone, without modeling their interaction explicitly. To address this limitation, this study proposes EDuHINet, an EDucational Heterogeneous Information Network that integrates cross‐sectional static features with assessment‐aligned temporal variables for multiclass online course performance prediction. Using 14,181 student records from OULAD, the model combines a temporal encoder, a static encoder, bilinear cross‐modal interaction, and hierarchical fusion to capture both evolving learning behavior and stable learner context. The prediction target is defined as a four‐category final course outcome classification, including Distinction, Pass, Fail, and Withdrawn. Experimental results show that EDuHINet achieves the strongest overall performance among the compared models, with advantages across overall accuracy, macro‐ and weighted‐level F‐score metrics, MCC, and Brier score, while maintaining competitive macro ROC AUC. Ablation experiments further indicate that joint modeling of the two modalities is more effective than using either modality alone, and early prediction results show that predictive information accumulates over the course trajectory. SHAP and ALE analyses show that late‐stage assessment scores strongly drive model predictions. Meanwhile, static variables like precourse engagement and study load provide essential contextual support. These findings indicate that online course performance prediction benefits from an interpretable bimodal framework that captures both temporal progression and learner context.

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