An Overview of Massive Open Online Course Platforms: Personalization and Semantic Web Technologies and Standards

Abstract

Abstract Massive Open Online Course (MOOC) is a form of online education that provides great learning capabilities. Semantic Web technologies is an appropriate mechanism for personalization in MOOC platforms. The aim of this paper is to find out how Semantic Web is used to facilitate personalization in modern MOOC platforms. The paper describes state-of-the-art MOOC platforms from the position of personalization and Semantic Web features. We defined five personalization and five Semantic Web criteria as well as 20 MOOC platforms to review. The personalization criteria includes a personalized learning path, personalized navigation, recommendation system, personalized assessment, and personalized feedback. The Semantic Web criteria includes ontology, Resource Description Framework (RDF), Web Ontology Language (OWL), SPARQL Protocol and RDF Query Language (SPARQL), and Linked Data. The results show that most of the platforms support personalized feedback. Half of the platforms has personalized learning path tools. One third of the platforms allow personalized assessment. Three platforms recommend learning materials, and one platform allows personalized navigation. The selected platforms have poor Semantic Web technologies and standards support: three platforms use ontologies and none of the platforms supports other criteria: RDF, OWL, SPARQL, and Linked Data. Personalization tools are supported better than Semantic Web tools. Most of the platforms have no support for Semantic Web criteria. This means that currently Semantic Web is not used for personalization in the reviewed MOOC platforms.