Using sensors and augmented reality to train apprentices using recorded expert performance: A systematic literature review

    Research output: Contribution to journalReview articleAcademicpeer-review

    Abstract

    Experts are imperative for training apprentices, but learning from experts is difficult. Experts often struggle to explicate and/or verbalize their knowledge or simply overlook important details due to internalization of their skills, which may make it more difficult for apprentices to learn from experts. In addition, the shortage of experts to support apprentices in one-to-one settings during trainings limits the development of apprentices. In this review, we investigate how augmented reality and sensor technology can be used to capture expert performance in such a way that the captured performance can be used to train apprentices without increasing the workload on experts. To this end, we have analysed 78 studies that have implemented augmented reality and sensor technology for training purposes. We explored how sensors have been used to capture expert performance with the intention of supporting apprentice training. Furthermore, we classified the instructional methods used by the studies according to the 4C/ID framework to understand how augmented reality and sensor technology have been used to support training. The results of this review show that augmented reality and sensor technology have the potential to capture expert performance for training purposes. The results also outline a methodological approach to how sensors and augmented reality learning environments can be designed for training using recorded expert performance.
    Original languageEnglish
    Pages (from-to)1-22
    Number of pages22
    JournalEducational Research Review
    Volume25
    DOIs
    Publication statusPublished - Nov 2018

    Keywords

    • Augmented reality
    • COMPOSITE LAYUP
    • ENVIRONMENT
    • Expert performance
    • FEEDBACK
    • KNOWLEDGE
    • LAPAROSCOPIC SURGERY
    • MIXED-REALITY
    • SIMULATION
    • SKILLS
    • Sensors
    • TRACKING
    • Training
    • VALIDATION

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