A Knowledge Graph-Based Framework for Personalized Course Recommendations in Higher Education

This paper presents a novel framework for university course recommendation, utilizing a pre-built knowledge graph derived from university-level training programs. The system incorporates essential learning rules, such as prerequisites, course dependencies, and credit limits. Furthermore, it applies reasoning over the knowledge graph to generate valid course pathways. The system considers each student’s learning outcomes, goals, and interests. Subsequently, it generates course recommendations tailored to individual students. These suggestions meet academic requirements and support future career goals. The proposed method’s effectiveness and consistency are evaluated by comparing its outcomes against expert recommendations. This approach provides a scalable and adaptable methodology to support academic decision-making and improve course selection in higher education.