Recommendation systems (RS) play a key role in e-learning by guiding learners toward relevant educational resources. Yet, the integration of domain knowledge to enhance both accuracy and explainability remains underexplored. This paper presents an explainable e-learning RS grounded in an educational Knowledge Graph (KG). The KG is constructed by extracting and linking key course concepts, and leveraged through embedding techniques to improve recommendation quality. To ensure transparency, we propose a path-based explanation mechanism that identifies and ranks user–course connections using a scoring function combining similarity measures and random walk probabilities. A case study demonstrates that the approach not only improves recommendation accuracy but also generates diverse, interpretable explanations, contributing to more transparent and trustworthy systems.