Online Course Recommendation System

Online learning platforms provide a wide range of courses, but learners may find it difficult to select suitable courses from largecatalogues. Generic course lists do not always reflect a learner’s goals, current skills, preferred learning style, available time, or priorlearning. This paper proposes an Online Course Recommendation System that organises course information and recommends relevantlearning options using learner preferences, course metadata, and interaction history where available. The proposed design combinescontent-based filtering with an optional collaborative-filtering component. Course descriptions and skill tags can be represented usingkeyword features or text embeddings, while learner profiles capture interests, experience level, preferred duration, and learning objectives.Recommendations should include understandable reasons, such as matching skills or selected topics, and should allow users to revise theirpreferences. The paper outlines the system architecture, data preparation, recommendation workflow, implementation plan, and evaluationmetrics such as Precision@K, Recall@K, NDCG@K, catalogue coverage, and response time. Privacy, cold-start problems, popularitybias, accessibility, and outdated course information are considered. This manuscript describes a proposed academic system and evaluationplan; it does not report experimental results because no prototype evaluation has been conducted for this paper.