Knowledge-Aware Two-Tower Framework for Enhanced Course Recommendation: Integrating Knowledge Management for Precision and Efficiency in Online Education

The rapid expansion of online education has intensified the need for precise and efficient course recommendation systems to address challenges like information overload and personalised learning. This study bridges three critical research gaps: (1) the lack of integration between knowledge management and deep learning in educational recommendations, (2) the persistent trade-off between recommendation accuracy and computational efficiency and (3) insufficient modelling of pedagogical relationships in existing systems. We propose a Knowledge-Aware Two-tower Framework (KATT) that synergises Knowledge-Aware Networks (KAN) with a lightweight attention mechanism to achieve both high performance (Area Under the Curve (AUC): 0.8645, Accuracy (ACC): 78.13%) and interpretability. Evaluations on the MoocCube dataset demonstrate KATT’s superiority over six state-of-the-art baselines, particularly in cold-start scenarios (15% improvement) and recommendation explainability. The framework’s balanced approach to pedagogical relevance and system efficiency offers a novel solution for scalable personalised education.