Linked open data for learning object discovery in adaptive e-learning systems

Abstract

This paper proposes a new approach to automatic retrieval of learning objects (LOs) from local or external LO repositories via linked open data (LOD) principles. The approach dynamically selects the most appropriate LOs for an individual learning package in an adaptive e-learning system based on the use of LO metadata, learner profiles, ontology, and LOD principles. The approach has been designed to interlink the domain ontology with external open knowledge in the LOD cloud. SPARQL endpoints for datasets in the LOD cloud are also provided for instructors and learners to discover their desired LOs. Moreover, commonly known vocabularies such as Dublin core (DC), IEEE learning object metadata (IEEE LOM), web ontology language (OWL), and resource description framework (RDF) are employed to represent metadata and to link it with external LO repositories as well as DBpedia, the central hub of the LOD cloud. By using these techniques, the LOs and external knowledge can be exchangeable, shareable, and interoperable, resulting in an enhanced access to better learning resources. Based on the proposed approach, a prototype system has been developed and evaluated. It has been discovered that the system has yielded positive effects in terms of the learners’ satisfaction.