A Survey on Linked Data and the Social Web as facilitators for TEL recommender systems

Stefan Dietze, Hendrik Drachsler, Giordano Daniela

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    Abstract

    Personalisation, adaptation and recommendation are central features of TEL environments. In this context, information retrieval techniques are applied as part of TEL recommender systems to filter and recommend learning resources or peer learners according to user preferences and requirements. However, the suitability and scope of possible recommendations is fundamentally dependent on the quality and quantity of available data, for instance, metadata about TEL resources as well as users. On the other hand, throughout the last years, the Linked Data (LD) movement has succeeded to provide a vast body of well-interlinked and publicly accessible Web data. This in particular includes Linked Data of explicit or implicit educational nature. The potential of LD to facilitate TEL recommender systems research and practice is discussed in this paper. In particular, an overview of most relevant LD sources and techniques is provided, together with a discussion of their potential for the TEL domain in general and TEL recommender systems in particular. Results from highly related European projects are presented and discussed together with an analysis of prevailing challenges and preliminary solutions.
    Original languageEnglish
    Title of host publicationRecommender Systems for Technology Enhanced Learning
    Subtitle of host publicationResearch Trends & Applications
    EditorsN. Manouselis, H. Drachsler, K. Verbert, O.C. Santos
    PublisherSpringer US
    Pages47-75
    ISBN (Electronic)978-1-4939-0530-0
    ISBN (Print)978-1-4939-0529-4, 978-1-4939-4656-3
    Publication statusPublished - 17 Dec 2014

    Keywords

    • Linked Data
    • Education
    • Semantic Web
    • Technology-Enhanced Learning
    • Data Consolidation
    • Data Integration

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