Recommender System for Delivering Learning Contents from a Digital Library to a Learning Management System
DOI:
https://doi.org/10.59994/ajbtme.2025.2.43Keywords:
Recommender Systems, University Courses, Digital Library, Learning Management System, Collaborative Filtering, Content-Based FilteringAbstract
This study enhances recommender systems (RSs) in digital libraries (DLs) integrated with learning management systems (LMSs), specifically Moodle at Palestine Ahliya University, to provide students with personalized educational resource recommendations. A hybrid RS combining content-based (CBF), rule-based (RBF), demographic-based (DBF), and collaborative filtering (CF) was developed. The methodology included content analysis of Moodle and DL resources, expert consultations, and user testing. The system, designed using a waterfall model, successfully prioritized recommendations based on title, keywords, subject, and user demographics, displaying results with relevance scores. User testing highlighted the need for continuous refinement. Practical implications include improved resource discovery efficiency and relevance, saving time and effort. The study underscores the importance of integrating RSs into LMSs and DLs to enhance learning experiences. The originality of this study lies in the integrated deployment of a multi-technique hybrid recommender system within a real university LMS–digital library environment, moving beyond isolated recommendation models. It also introduces a priority-based academic relevance mechanism that aligns recommendations with course content, learner characteristics, and digital library metadata.
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