Application of Resource Description Framework to Personalise Learning: Systematic Review and Methodology

The paper is aimed to present a methodology of learning personalisation based on applying Resource Description Framework (RDF) standard model. Research results are two-fold: first, the results of systematic literature review on Linked Data, RDF "subject-predicate-object" triples, and Web Ontology Language (OWL) application in education are presented, and, second, RDF triples-based learning personalisation methodology is proposed. The review revealed that OWL, Linked Data, and triples-based RDF standard model could be successfully used in education. On the other hand, although OWL, Linked Data approach and RDF standard model are already well-known in scientific literature, only few authors have analysed its application to personalise learning process, but many authors agree that OWL, Linked Data and RDF-based learning personalisation trends should be further analysed. The main scientific contribution of the paper is presentation of original methodology to create personalised RDF triples to further development of corresponding OWL-based ontologies and recommender system. According to this methodology, RDF-based personalisation of learning should be based on applying students' learning styles and intelligent technologies. The main advantages of this approach are analyses of interlinks between students' learning styles according to Felder-Silverman learning styles model and suitable learning components (learning objects and learning activities). There are three RDF triples used while creating the methodology: "student's learning style - requires - suitable learning objects", "student's learning style - requires - suitable learning activities", and "suitable learning activities - require - suitable learning objects". In the last triple, "suitable learning activities" being the object in the 2nd triple, becomes the subject in the 3rd triple. The methodology is based on applying pedagogically sound vocabularies of learning components (i.e. learning objects and learning activities), experts' collective intelligence to identify learning objects and learning methods / activities that are most suitable for particular students, and intelligent technologies (i.e. ontologies and recommender system). This methodology based on applying personalised RDF triples is aimed at improving learning quality and effectiveness.