Dynamic Peer Recommendation System based on Trust Model for sustainable social tutoring in MOOCs

Abstract

The choice of the relevant partners to interact and collaborate is a sensitive issue, especially in open, dynamic and heterogeneous environments such as the MOOC platforms marked by a large number of learners having different needs and expectations. In these contexts, the process of detecting the trusted user to interact and collaborate is more difficult and time-consuming which can be contributor to learner’s demotivation and disengagement.Trust models could be efficiently adopted in these platforms to increase completion rates and to boost learner’s motivation by helping them to find the appropriate partner (peer) for meeting their needs and achieving their learning objectives. In general, several methods used to recommend peers in MOOCs are based particularly on similarity between learners profiles without considering the dynamicity of their behaviors and interests, or the influence of the trust relationships among them which impact strongly the selection of the suitable partner.In this paper we provide architecture of Dynamic Peer Recommendation System (DPRS) based on trust management system (TMS) that represent an adaptation and inspiration of one of the major and recent works of dynamic SIoT trust models in order to adjust to the high level of dynamism and mobility of MOOCs. This architecture eases the decision making process to select relevant partner by MOOC learners to guarantee an engaging learning experience and promote a peer-collaboration in this community.