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    Towards Competence-Based Learning Design Driven Remote and Virtual Labs Recommendations for Science Teachers

    Access Status
    Fulltext not available
    Authors
    Zervas, P.
    Sergis, S.
    Sampson, Demetrios
    Fyskilis, S.
    Date
    2015
    Type
    Journal Article
    
    Metadata
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    Citation
    Zervas, P. and Sergis, S. and Sampson, D. and Fyskilis, S. 2015. Towards Competence-Based Learning Design Driven Remote and Virtual Labs Recommendations for Science Teachers. Technology, Knowledge and Learning. 20 (2): pp. 185-199.
    Source Title
    Technology, Knowledge and Learning
    DOI
    10.1007/s10758-015-9256-6
    ISSN
    2211-1662
    School
    School of Education
    URI
    http://hdl.handle.net/20.500.11937/39503
    Collection
    • Curtin Research Publications
    Abstract

    Remote and virtual labs (RVLs) are widely used by science education teachers in their daily teaching practice. This has led to a plethora of RVLs that are offered with or without cost. In order to organise them and facilitate their search and findability, several RVL web-based repositories have been operated. As a result, a key open challenge is to design and implement recommender systems that support science teachers in finding and selecting appropriate RVLs based on core pedagogical elements of their learning designs and their own information and communication technology (ICT)-supported teaching competence profiles. In response to this challenge, we propose a recommender system for supporting learning design driven RVLs recommendations taking into consideration teachers’ profiling elements related to the ICT-supported teaching competences for using RVLs. To achieve this, we propose an instantiation of the UNESCO ICT competency framework for teachers for the context of using RVL and capitalise on our previously proposed metadata schema for describing RVLs, which includes core elements of a learning design and teachers’ required ICT-supported teaching competences. Our evaluation results based on a user study, highlight the high level of ranking accuracy of our proposed recommender system and support its capacity to facilitate science teachers in selecting appropriate RVLs.

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