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Team-Based Learning Analytics: An Empirical Case Study.

Ying Yun Juliana Koh1, Henk G Schmidt, Naomi Low-Beer

  • 1Y.Y.J. Koh is research associate, Medical Education and Scholarship Unit, Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore; ORCID: https://orcid.org/0000-0003-2062-5617. H.G. Schmidt is professor of psychology, Institute of Medical Education Research, Erasmus University Medical Center, Rotterdam, the Netherlands; ORCID: https://orcid.org/0000-0001-8706-0978. N. Low-Beer is professor and director, Medical Education and Scholarship Unit, Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore; ORCID: https://orcid.org/0000-0002-6801-0091. J.I. Rotgans is assistant professor, Medical Education and Scholarship Unit, Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore; ORCID: https://orcid.org/0000-0003-4043-8261.

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Summary
This summary is machine-generated.

Learning management systems (LMS) can identify struggling teams in medical education. LMS data revealed one student

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Area of Science:

  • Medical Education
  • Educational Technology
  • Learning Analytics

Background:

  • Team-based learning (TBL) is widely adopted in medical schools.
  • Learning management systems (LMS) support TBL but their data potential is often underutilized.
  • Routinely collected LMS data can offer insights into educational processes.

Purpose of the Study:

  • To illustrate how LMS data can be used for evidence-based decision-making in medical education.
  • To investigate the root cause of a struggling team's performance in a TBL module.

Main Methods:

  • A case study approach was employed at a medical school.
  • Four analyses were conducted using data from the institution's LMS.
  • Data analysis focused on team performance during TBL sessions and readiness assurance tests (tRAT).

Main Results:

  • A specific team demonstrated significantly poorer performance on the tRAT.
  • This poorer performance was consistent across multiple TBL sessions within a module.
  • LMS data analysis identified a lack of individual team member preparation as the cause, not collective team failure.

Conclusions:

  • LMS data are a powerful resource for identifying and addressing educational challenges.
  • Systematic analysis of LMS data can support evidence-based educational decision-making.
  • Leveraging LMS data can lead to targeted interventions for improving team and individual performance in TBL.