On methodology of application of linked data to personalise learning

Abstract

The paper is aimed to present a methodology of learning personalisation based on applying Semantic Web technologies and particularly Linked Data. Semantic Web technologies and Linked Data are changing the way information is stored, described and exploited. The “Linked Data” term refers to a set of best practices for publishing and connecting structured data on the Web. The advantages of Linked Data web are used to support semi-automatic classification of educational resources. The relations of learning objects (resources) are encoded in Resource Description Language and stored in the repository, a query language is used to retrieve data, and the knowledge of organisational systems and Linked Data is used to classify the web resources according to the domain The Linked Data principles are applied for semantic integration and social interconnecting of educational data, resources and actors. Linked Data movement promises to significantly improve existing practices of system integration, resource sharing and personalisation to support learning. The Linked Data approach is a promising approach to establish relationships between learning resources and student’s personal characteristics. Linked Data approach and Resource Description Framework (RDF) standard model are already well-known in scientific literature, but only few authors have analysed its application to personalise learning process. Many authors agree that OWL, Linked Data, ontologies, recommender systems, and RDF-based learning personalisation trends should be further analysed. In the paper, first of all, systematics review on application of Semantic Web and particularly Linked Data to personalise learning is presented. After that, methodology how to apply Linked Data and the other Semantic Web technologies such as RDF triples, OWL, ontologies, and recommender systems to personalise learning is presented. This personalisation should be based on applying students’ personal preferences (e.g. learning styles) and intelligent technologies. Interconnections between students’ learning styles and suitable learning components (i.e. learning objects and learning activities) are analysed in the paper in more detail. According to presented methodology, after identifying particular students’ learning styles and particular learning components’ (learning objects’ and learning activities’) suitability indexes, one could create a number of analysed RDF triples, corresponding OWL-based ontologies and, finally, a recommender system to recommend learners those learning components that fit their personal preferences mostly. Probabilistic suitability indexes applied in the paper show the level of suitability of learning components to particular students and are based on probabilistic analysis of particular students’ learning styles as well as on analysis of correspondence of particular learning components to learning styles. This methodology based on applying Semantic Web technologies is aimed at improving learning motivation and thus – learning quality and effectiveness.