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Leveraging Digital Trace Data in Teaching to Improve Students’ Technology Use and Well-Being

Author

Listed:
  • Martin Adam

    (University of Göttingen)

  • Alexander Benlian

    (Fachgebiet Wirtschaftsinformatik: Information Systems & E-Services Fachbereich 1—Rechts- und Wirtschaftswissenschaften, University of Darmstadt)

Abstract

Teaching is witnessing a burgeoning interest in the application of digital trace data, particularly driven by its capability to unveil intricate patterns in student behavior and bolster personalized learning experiences. But how exactly can digital trace data be incorporated into teaching, especially to allow students to learn from their own digital trace data. Through this case study, we introduce a seamless strategy for utilizing digital trace data, from data generation through the tracing process to the extraction of insightful data-centric findings. Specifically, we present an annually recurring, application-focused university course that addresses how teachers can leverage digital trace data, data tools, and data analytics to help students understand how they feel and behave so that they can demonstrably increase their own (subjective) well-being and build more productive (objective) habits using digital technologies. Thus, we offer practical lessons and easily implementable directions for teachers to enable them to draw on digital trace data in their classes to help students better grasp technology use, improve their well-being, and personalize their learning.

Suggested Citation

  • Martin Adam & Alexander Benlian, 2026. "Leveraging Digital Trace Data in Teaching to Improve Students’ Technology Use and Well-Being," Progress in IS,, Springer.
    • Handle: RePEc:spr:prochp:978-3-032-05497-5_13
    DOI: 10.1007/978-3-032-05497-5_13
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