Adapting Pedagogical Objects to Learning Styles Through Recommendation Systems in E-Learning

Abstract

The adaptation of pedagogical object to learning styles has become crucial for maximizing the effectiveness of e-learning environments. Learners have unique preferences and ways of assimilating knowledge, making it necessary to personalize learning paths to accommodate this diversity. Learning style models provide a theoretical framework to understand these differences and develop tailored pedagogical strategies. This chapter explores how recommendation systems can be used to align pedagogical object with learner profiles. By utilizing advanced algorithms such as K-means clustering, these systems analyze learner interactions and preferences to suggest personalization pedagogical object. This approach aims to create more engaging and effective learning environments that can meet the specific needs of each learner.