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Publication Detail
Framing Professional Learning Analytics as Reframing Oneself
  • Publication Type:
    Journal article
  • Publication Sub Type:
    Article
  • Authors:
    Shum SB, Littlejohn A, Kitto K, Crick R
  • Publisher:
    Institute of Electrical and Electronics Engineers (IEEE)
  • Publication date:
    19/07/2022
  • Pagination:
    1, 17
  • Journal:
    IEEE Transactions on Learning Technologies
  • Status:
    Published
  • Print ISSN:
    1939-1382
  • Language:
    English
Abstract
Central to imagining the future of technology-enhanced professional learning is the question of how data are gathered, analyzed, and fed back to stakeholders. The field of learning analytics (LA) has emerged over the last decade at the intersection of data science, learning sciences, human-centered and instructional design, and organizational change, and so could in principle inform how data can be gathered and analyzed in ways that support professional learning. However, in contrast to formal education where most research in LA has been conducted, much work-integrated learning is experiential, social, situated, and practice-bound. Supporting such learning exposes a significant weakness in LA research, and to make sense of this gap, this article proposes an adaptation of the Knowledge-Agency Window framework. It draws attention to how different forms of professional learning locate on the dimensions of learner agency and knowledge creation. Specifically, we argue that the concept of “reframing oneself” holds particular relevance for informal, work-integrated learning. To illustrate how this insight translates into LA design for professionals, three examples are provided: first, analyzing personal and team skills profiles (skills analytics); second, making sense of challenging workplace experiences (reflective writing analytics); and third, reflecting on orientation to learning (dispositional analytics). We foreground professional agency as a key requirement for such techniques to be used effectively and ethically.
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