Peer Reviewer Recommendation in Online Social Learning Context: Integrating Information of Learners and Submissions

With the rapid development of massive open online courses, peer assessment has played an important role in promoting social learning. According to social learning theory, peer assessment in online courses provides students a chance to learn from each other when they review other students’ submissions, which motivates their participation in online social learning. However, existing peer assessment cannot generate satisfactory results in that a systematic approach to find peer reviewers for submissions is lacked. To address this problem, this paper proposes a reviewer recommendation system which can suggest appropriate reviewers for submissions during the process of peer assessment. This recommendation system is built by integrating information of learners and submissions. The integration of this reviewer recommendation system in the process of peer assessment may help to improve learners’ satisfaction and their learning performance. Additionally, the reviewer recommendation system can also be used in many other fields such as enterprise training or language learning online.