A Modified Hybrid Content-Aware Course Recommendation Model for Moodle-Based Learning Management System

Existing course recommendation systems in Learning Management Systems (LMS) often restrict suggestions to the same categories as a user's previously taken courses, limiting diverse course discovery. To address this, this research developed a personalized, cross-category course recommendation system for a data-constrained institutional LMS. This research adapted the Hybrid Content-Aware Course Recommendation (HCACR) framework, integrating a metadata-based user-interest model, a K-Modes-based demographic characteristic model, and a sequential course history model to mitigate data sparsity and cold-start problems. The system was deployed in a Moodle-based environment and evaluated by 171 users. Experimental results show that the model achieved a precision of 26.78% and a recall of 31.07%, which are reasonable given the data constraints in internal government education contexts. Crucially, the system obtained an excellent System Usability Scale (SUS) score of 86.25, indicating high user satisfaction despite the moderate algorithmic precision. While the reliance on sparse metadata limits semantic richness compared to full-content models, this study demonstrates that a hybrid approach is a feasible and effective solution for enhancing course discovery in institutional settings with limited data access.