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Addressing Learners’ Heterogeneity in Higher Education: An Explainable AI-Based Feedback Artifact for Digital Learning Environments

Author

Listed:
  • Felix Haag

    (University of Bamberg)

  • Sebastian A. Günther

    (University of Bamberg)

  • Konstantin Hopf

    (University of Bamberg)

  • Philipp Handschuh

    (Leibniz Institute for Educational Trajectories)

  • Maria Klose

    (Leibniz Institute for Educational Trajectories)

  • Thorsten Staake

    (University of Bamberg)

Abstract

Due to the advent of digital learning environments and the freedom they offer for learners, new challenges arise for students’ self-regulated learning. To overcome these challenges, the provision of feedback has led to excellent results, such as less procrastination and improved academic performance. Yet, current feedback artifacts neglect learners’ heterogeneity when it comes to prescriptive feedback that should meet personal characteristics and self-regulated learning skills. In this paper, we derive requirements from self-regulated learning theory for a feedback artifact that takes learners’ heterogeneity into account. Based on these requirements, we design, instantiate, and evaluate an Explainable AI-based approach. The results demonstrate that our artifact is able to detect promising patterns in data on learners’ behaviors and characteristics. Moreover, our evaluation suggests that learners perceive our feedback as valuable. Ultimately, our study informs Information Systems research in the design of future Explainable AI-based feedback artifacts that seek to address learners’ heterogeneity.

Suggested Citation

  • Felix Haag & Sebastian A. Günther & Konstantin Hopf & Philipp Handschuh & Maria Klose & Thorsten Staake, 2025. "Addressing Learners’ Heterogeneity in Higher Education: An Explainable AI-Based Feedback Artifact for Digital Learning Environments," Lecture Notes in Information Systems and Organization,, Springer.
    • Handle: RePEc:spr:lnichp:978-3-031-80125-9_30
    DOI: 10.1007/978-3-031-80125-9_30
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