Personalized Learning Path Recommendation System

Authors: David ray, Shubham Kumar, Vikash Kumar,, Deepjit Ganguly, Sudhanshu Choudhary

Abstract: The rapid expansion of digital learning resources has created both opportunities and challenges for learners. While access to massive open online courses, learning management systems, and skill-based platforms has increased, learners often struggle to identify the most suitable sequence of learning activities aligned with their goals, prior knowledge, and learning preferences. This research paper proposes a Personalized Learning Path Recommendation System (PLPRS) that dynamically recommends learning paths tailored to individual learners. The proposed system integrates collaborative filtering, content-based filtering, and learner profiling techniques to generate adaptive and goal-oriented learning pathways. Experimental evaluation using standard recommender system metrics demonstrates that the proposed approach improves recommendation relevance, learner engagement, and learning efficiency. The study highlights the applicability of personalized learning path systems in higher education and e-learning platforms and outlines future directions for hybrid and AI-driven learning recommendations.

DOI: https://doi.org/10.5281/zenodo.20455927