Recommender System to Support MOOCs Teachers: Framework based on Ontology and Linked Data

Abstract

The proliferation of Massive Open Online Courses (MOOCs) has generated conflicting opinions about their quality. In this paper, we aim at improving the quality of MOOCs through assisting teachers and designers from the initiation phase of MOOCs. For this purpose, we propose a recommendation system Framework based on the knowledge about teachers and MOOCs. Our approach aims to overcome the problems of traditional recommendation systems, by using and integrating different techniques: modeling via ontologies, semantic web technologies, extracting and integrating Linked Data from different sources, ontology mapping and semantic similarity measures.