Conference

A framework to provide personalization in learning management systems through a recommender system approach

This paper explores personalization in Learning Management Systems (LMS) by integrating recommender systems to tailor learning experiences based on learner profiles. Traditional LMSs present content statically, ignoring factors like learning styles, goals, prior knowledge, abilities, and interests. The proposed framework introduces a flexible integration model that suggests learning objects based on both individual learner data and successful learning patterns from similar profiles. This approach enhances learning efficiency, performance, and satisfaction, reducing learning time while maintaining academic success.