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Socio-semantic Integration of Educational Resources - the Case of the mEducator Project
Stefan Dietze (Leibniz University, Germany)
Eleni Kaldoudi (Democritus University of Thrace, Greece)
Nikolas Dovrolis (Democritus University of Thrace, Greece)
Daniela Giordano (University of Catania, Italy)
Concetto Spampinato (University of Catania, Italy)
Maurice Hendrix (Coventry University, United Kingdom)
Aristidis Protopsaltis (Coventry University, United Kingdom)
Davide Taibi (Italian National Research Council, Italy)
Hong Qing Yu (The Open University, United Kingdom)
Abstract: Research in technology-enhanced learning (TEL) throughout the last decade has largely focused on sharing and reusing educational resources and data. This effort has led to a fragmented landscape of competing metadata schemas, such as IEEE LOM or ADL SCORM, and interface mechanisms, such as OAI-PMH, SQI and REST-ful services in general. More recently, semantic technologies were taken into account to improve interoperability. However, so far Web-scale integration of resources is not facilitated, mainly due to the lack of take-up of shared principles, datasets and schemas. On the other hand, the Linked Data approach has emerged as the de facto standard for sharing data on the Web and is fundamentally based on established W3C standards. This paper presents results of the European Commission-funded project mEducator, which exploits Linked Data principles for (1) semantic integration and (2) social interconnecting of educational data, resources and actors. We describe a general approach to exploit the wealth of already existing educational data on the Web by allowing its exposure as Linked Data and by taking into account automated enrichment and interlinking techniques to provide a rich and well-interlinked graph for the educational domain. Additionally, the paper presents an evaluation of our work with respect to a set of socio-semantic dimensions. Experimental results demonstrate improved interoperability and retrievability of the resulting resource descriptions as part of an interlinked resource graph.
Keywords: SOA, clustering, linked data, semantic web, technology-enhanced learning
Categories: H.3, H.4, H.5, M.1, M.4, M.7, M.8, M.9
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