Supporting Training of Expertise with Wearable Technologies: The WEKIT Reference Framework

Bibeg Limbu, Mikhail Fominykh, Roland Klemke, Marcus Specht, Fridolin Wild

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    In this chapter, we present a conceptual reference framework for designing augmented reality applications for supporting training. The framework leverages the capabilities of modern augmented reality and wearable technology for capturing the expert’s performance in order to support expertise development. It has been designed in the context of Wearable Experience for Knowledge Intensive Training (WEKIT) project which intends to deliver a novel technological platform for industrial training. The framework identifies the state-of-the-art augmented reality training methods, which we term as “transfer mechanisms” from an extensive literature review. Transfer mechanisms exploit the educational affordances of augmented reality and wearable technology to capture the expert performance and train the trainees. The framework itself is based upon Merrienboer’s 4C/ID model which is suitable for training complex skills. The 4C/ID model encapsulates major elements of apprenticeship models which is a primary method of training in industries. The framework complements the 4C/ID model with expert performance data captured with help of wearable technology which is then exploited in the model to provide a novel training approach for efficiently and effectively mastering the skills required. In this chapter, we will give a brief overview of our current progress in developing this framework.
    Original languageEnglish
    Title of host publicationMobile and Ubiquitous Learning
    Subtitle of host publicationPerspectives on Rethinking and Reforming Education
    EditorsS. Yu, M. Ally, A. Tsinakos
    PublisherSpringer Singapore
    ISBN (Electronic)978-981-10-6144-8
    ISBN (Print)978-981-10-6143-1
    Publication statusPublished - 2018

    Publication series

    SeriesPerspectives on Rethinking and Reforming Education


    • Expertise
    • Augmented reality
    • Sensors
    • Training


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