Affiliation:
1. Faculty of Sciences Tetuan, Abdelmalek Esaadi University, Morocco
2. Ecole Normale Supérieure, Abdelmalek Essaadi University, Morocco
Abstract
Educational resource recommendation systems have grown significantly, providing innovative ways to enrich student learning. Utilizing sophisticated algorithms, these systems help learners discover relevant materials, aiding their academic goals. This chapter explores a recommendation application tailored for the edX platform, a renowned MOOC platform. The application features an advanced recommendation algorithm, distinct from traditional methods, focusing on various learner data aspects like history, preferences, and cognitive aptitude. Employing machine learning techniques, it offers personalized suggestions, enhancing the learning experience. The algorithm's integration and effectiveness are tested on the edX CMS, evaluated across metrics like accuracy, cognitive development, learner engagement, and retention. This assessment underscores its adaptability to different learning styles, showcasing its role in advancing personalized learning in MOOC environments.
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