Reciprocal peer recommendation for learning purposes
Boyd A. Potts, Hassan Khosravi, Carl Reidsema, Aneesha Bakharia, Mark Belonogoff, Melanie Fleming · 2018
Larger student intakes by universities and the rise of education through Massive Open Online Courses has led to less direct contact time with teaching staff for each student. One potential way of addressing this contact deficit is to invite learners to engage in peer learning and peer support; however, without technological support they may be unable to discover suitable peer connections that can enhance their learning experience. Two different research subfields with ties to recommender systems provide partial solutions to this problem. Reciprocal recommender systems provide sophisticated filtering techniques that enable users to connect with one another. To date, however, the main focus of reciprocal recommender systems has been on providing recommendation in online dating sites. Recommender systems for technology enhanced learning have employed and tailored exemplary recommenders towards use in education, with a focus on recommending learning content rather than other users. In this paper, we first discuss the importance of supporting peer learning and the role recommending reciprocal peers can play in educational settings. We then introduce our open-source course-level recommendation platform called RiPPLE that has the capacity to provide reciprocal peer recommendation. The proposed reciprocal peer recommender algorithm is evaluated against key criteria such as scalability, reciprocality, coverage, and quality and shows improvement over a baseline recommender. Primary results indicate that the system can help learners connect with peers based on their knowledge gaps and reciprocal preferences, with designed flexibility to address key limitations of existing algorithms identified in the literature.