A Data-Driven Approach for Peer Recommendation to Reduce Dropouts in MOOC

摘要

Massive open online course (MOOC) is an online mode for learning aimed at unlimited participation. A characteristic feature of MOOC is reduced availability of social interaction, which is often responsible for learners feeling isolated. Although to facilitate interaction, MOOC has functionalities like discussion forum, group assignment and peer grading; however, to use these functionalities, the learner has to extensively search for the right person to interact from a large pool of learners. The isolation among learners is one of the significant factors contributing to high learner dropout rate, a major concern for MOOC. In this paper, we present an approach to reduce the dropout rate of MOOC by solving the problem of isolation. A potential solution to this problem is to encourage peer learning, by supporting learners to find other learners for interaction purposes. In this paper, we propose a user similarity-based peer recommendation approach that makes use of learners' scores and their demographic attributes, to provide recommendations on potential learning peers. To date, however, the main focus of traditional approaches for peer recommendations is on providing recommendations to all learners, including the ones who were not feeling isolated. Furthermore, these approaches provide peer recommendations to learners without considering their actual cause of isolation. To overcome these limitations, we use adaptive interventions to first identify the isolated learners and then recommend peer learners based on their cause of isolation. The proposed approach for peer recommendation is evaluated on the basis of scalability and coverage. The publicly available MIT Harvard database has been used for experimental purpose.