With the rapid evolution of online education, the demand for personalized learning experiences has grown significantly. This paper introduces an innovative approach to enhance e-learning platforms through the integration of a machine learning-based framework. The proposed system focuses on optimizing the Recommender System, leveraging Recommender Systems techniques to tailor educational content to individual learner profiles. Through the analysis of user behavior and content features, our framework dynamically adapts recommendations, aiming to improve user engagement, knowledge retention, and overall learning outcomes. The study explores the efficacy of machine learning models, real-time adaptation mechanisms, and user feedback loops to create a comprehensive Recommender Systems Platform. Preliminary results indicate promising advancements in personalization and effectiveness, offering a pathway for the future development of intelligent and responsive e-learning environments
Hannah Louise Carter (Mon,) studied this question.