PERSONALIZED E-LEARNING COURSE RECOMMENDATION SYSTEM

Abstract

The rapid expansion of digital education platforms has transformed the way learners access knowledge, enabling flexible and remote learning opportunities across the globe. However, the exponential growth in the number of available online courses has created a significant challenge for learners in identifying relevant and high-quality content that matches their interests, skill levels, and learning goals. This issue often leads to information overload, reduced learner engagement, and inefficient course selection. To address these challenges, this paper proposes a Personalized E-Learning Course Recommendation System that leverages machine learning techniques to provide tailored course suggestions. The proposed system integrates collaborative filtering and content-based filtering approaches to improve recommendation accuracy and overcome the limitations of individual methods. Collaborative filtering analyzes user behavior and similarities among users, while content-based filtering focuses on course attributes and user preferences. By combining these techniques into a hybrid model, the system generates highly relevant recommendations.