Unpacking the Complexity: Why Current Feedback Systems Fail to Improve Learner Self-Regulation of Participation in Collaborative Activities

Xavier Ochoa, Xiaomeng Huang, Adam Charlton

Research output: Contribution to journalArticlepeer-review

Abstract

Even before the inception of the term learning analytics, researchers globally had been investigating the use of various feedback systems to support the self-regulation of participation and promote equitable contributions during collaborative learning activities. While some studies indicate positive effects for distinct subgroups of learners, a common finding is that the majority of learners do not modify their behaviour, even after repeated interventions. In this paper, we assessed one such system and, predictably, did not find measurable improvements in equitable participation. Informed by self-regulated learning theory, we conducted a mixed-methods study to explore the diverse paths that learners take in the self-regulation process initiated by the feedback. We found that the observed deviations from the expected path explain the difficulty in measuring a generalized effect. This study proposes a shift in research focus from merely improving the technological aspects of the system to a human-and pedagogical-centred redesign that takes special consideration of how learners understand and process feedback to self-regulate their participation.

Original languageEnglish (US)
Pages (from-to)246-267
Number of pages22
JournalJournal of Learning Analytics
Volume11
Issue number2
DOIs
StatePublished - Aug 18 2024

Keywords

  • Equitable participation
  • feedback systems
  • self-regulated learning

ASJC Scopus subject areas

  • Education
  • Computer Science Applications

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