Human-centered approaches to data-informed feedback

Research output: Chapter in Book/Report/Conference proceedingChapter (Book)Researchpeer-review

Abstract

Learning analytics seeks to support and enhance learning through data-informed feedback
practices. As learning analytics emphasizes an iterative loop from learner to data, metrics, and
interventions, it is imperative that both teachers and learners play active roles in this process and
contribute to the design and evaluation of enabling technologies. A key question that concerns
us is: How can learning analytics tools enhance learners’ agency in the feedback process? We argue
that the design and deployment of learning analytics need to recognize feedback as a dialogic
process. In doing so, we emphasize that effective feedback is not just about providing information
relevant to learning, but also about the practices of the people who carry out evaluations and
produce or interpret information based on such evaluations. A human-centered approach is thus
critical to the effectiveness of data-informed feedback. In this chapter we discuss key elements of
feedback, current approaches to data-informed feedback and associated challenges; and propose
a human-centered approach which facilitates collaborative learning and continuous learning
among a network of actors and highlights the importance of developing data-informed feedback
literacy among learners.
Original languageEnglish
Title of host publicationHandbook of Learning Analytics
EditorsCharles Lang, George Siemens, Alyssa Friend Wise, Dragan Gasevic, Agathe Merceron
Place of PublicationNew York NY USA
PublisherSociety for Learning Analytics Research
Chapter21
Pages213-222
Number of pages10
Edition2nd
ISBN (Electronic)9780995240834
Publication statusPublished - 2022

Keywords

  • Feedback
  • co-design
  • learning analytics
  • human-centered
  • data

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