Reciprocal Recommender System for Learners in Massive Open Online Courses (MOOCs)
This addresses the challenge of facilitating communication among diverse learners in MOOCs, though it is incremental as it builds on existing recommender system methods.
The paper tackles the problem of hesitant learners in MOOCs by proposing a reciprocal recommender system that matches learners based on profile attributes, demonstrating that attribute importance and reciprocity are key factors in forming recommendations.
Massive open online courses (MOOC) describe platforms where users with completely different backgrounds subscribe to various courses on offer. MOOC forums and discussion boards offer learners a medium to communicate with each other and maximize their learning outcomes. However, oftentimes learners are hesitant to approach each other for different reasons (being shy, don't know the right match, etc.). In this paper, we propose a reciprocal recommender system which matches learners who are mutually interested in, and likely to communicate with each other based on their profile attributes like age, location, gender, qualification, interests, etc. We test our algorithm on data sampled using the publicly available MITx-Harvardx dataset and demonstrate that both attribute importance and reciprocity play an important role in forming the final recommendation list of learners. Our approach provides promising results for such a system to be implemented within an actual MOOC.