An Ontology-based Recommender System for Identifying Learners’ Confusion in MOOCs

Abstract

The rapid spread of MOOCs (massive open online courses) allows learners to benefit from these courses by having access to quality education. However, MOOCs experience high dropout rates due largely to learners’ confusion of some basic concepts. This article explores the use of an ontology-based recommender system to identify the degree of confusion related to a specific concept. For each concept cited in a post extracted from MOOC discussion forums, the degree of confusion is analyzed using our ontology and classification algorithm. Therefore, our primary objective is to extract and analyze all the prerequisite information needed to understand a given concept in order to develop an adaptive learning model and engage learners through the understanding of the course material.