Journal

PLORS: a personalized learning object recommender system

This paper explores how traditional Learning Management Systems (LMS) support course administration but often follow a one-size-fits-all approach, disregarding individual learner profiles such as learning styles, goals, prior knowledge, abilities, and interests. To enhance personalization, recommender systems can suggest relevant learning objects based on a learner’s activity and similar profiles. The proposed personalized learning object recommender system provides tailored content recommendations, helping learners discover valuable resources they may have otherwise overlooked. This personalization enhances engagement, performance, and overall learning experience.