Improving Course Recommendations: An RBM-Based Explainable Framework for Student Performance in E-Learning
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Abstract
The vast expansion of online learning has increased the need for intelligent systems to guide learners toward suitable learning paths. Although educational recommender systems address this challenge by personalizing recommendations, many existing approaches focus primarily on accuracy and provide limited insight into why recommendations are made. This study proposes EduExplain, an explainable collaborative filtering framework based on a Restricted Boltzmann Machine (RBM) that combines strong predictive performance with neighbor-similarity explanations to generate interpretable recommendations. The framework integrates a prediction engine with an explanation module that produces natural-language justifications derived from the performance of similar learners. We evaluated the framework using a multi-domain experimental design on the OULAD educational dataset and the Goodreads dataset, comparing it against four established baselines. The results show that EduExplain achieves a 20% reduction in prediction error over traditional collaborative filtering (RMSE: 0.873 on OULAD) and delivers high-quality, interpretable explanations (coherence: 0.89), demonstrating that accuracy and explainability can coexist in e-learning systems. Unlike prior approaches that rely on external review data or treat explainability as a post-hoc step, EduExplain embeds explanation generation directly into the RBM pipeline using only interaction data. An ablation study confirmed each component's necessity for both accuracy and interpretability. These findings demonstrate that explainable recommender systems can enhance decision-making in e-learning environments.
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