The increasing reliance on artificial intelligence (AI) in education has highlighted the necessity for transparent and trustworthy recommender systems. This paper presents a framework that combines collaborative filtering, specifically Singular Value Decomposition (SVD), with post-hoc, model-agnostic explanation techniques to generate interpretable recommendations in e-learning environments. To achieve this, we implement both crisp and fuzzy association rule mining approaches for explaining recommendations based on learners’ historical behaviors. The fuzzy model leverages membership degrees to account for interaction intensity, while the crisp model uses binarized user–item transactions. We generate explanations by mapping recommended items to association rules, and explanation quality is assessed using the Average Fidelity metric. Experimental evaluations on a real-world MOOC dataset demonstrate the effectiveness of the framework in delivering recommendations with interpretable justifications. The results validate the feasibility of integrating fuzzy-based explainability into recommendation pipelines, as the proposed approach enhances the crisp-based explanation approach, taking into account fidelity-based measures.