Enhanced Scenario Model for Peer Assessment in iMOOCs Based on Semantic Web

Abstract

MOOCs (Massive Open Online Courses), a new form of learning, are characterized by a large scale of subscription. Thus, appropriate assessment strategies should be implemented to support massive data flows. In this paper, we are interested in studying the Peer Assessment in MOOCs based on Inquiry Based Learning (IBL), iMOOCs. Our contribution is a continuation of our previous research work. In fact, IBL allows the learner to be involved in the analysis of a given problem, related to practical facts and the search for possible solutions. To do this, we have implemented ontological models in order to ensure interoperability at the semantic level. Our scenario and models are validated through integration in the developed platform SMOOPLE (Semantic Massive Open Online Personal / Pervasive Learning Environment).