Reciprocal Recommender System for Learners in Massive Open Online Courses (MOOCs)

Sankalp Prabhakar, Gerasimos Spanakis, Osmar Zaiane

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingAcademicpeer-review

Abstract

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.
Original languageEnglish
Title of host publicationInternational Conference on Web-Based Learning (ICWL)
PublisherSpringer
Pages157-167
Number of pages11
DOIs
Publication statusPublished - Sep 2017

Cite this

Prabhakar, S., Spanakis, G., & Zaiane, O. (2017). Reciprocal Recommender System for Learners in Massive Open Online Courses (MOOCs). In International Conference on Web-Based Learning (ICWL) (pp. 157-167). Springer. https://doi.org/10.1007/978-3-319-66733-1_17