Team-based learning analytics: an empirical case study

• Main Authors: Koh, Juliana Ying Yun, Schmidt, Henk G., Low-Beer, Naomi, Rotgans, Jerome Ingmar
• Other Authors: Lee Kong Chian School of Medicine (LKCMedicine)
• Format: Article
• Language: English
• Published: 2022
• Subjects: Science::Medicine; Technology; Impact
• Online Access: https://hdl.handle.net/10356/161132
• Institution: Nanyang Technological University

Many medical schools that have implemented team-based learning (TBL) have also incorporated an electronic learning architecture, commonly referred to as a learning management system (LMS), to support the instructional process. However, one LMS feature that is often overlooked is the LMS's ability to record data that can be used for further analysis. In this article, the authors present a case study illustrating how one medical school used data that are routinely collected via the school's LMS to make informed decisions. The case study started with one instructor's observation that some teams in one of the undergraduate medical education learning modules appeared to be struggling during one of the team activities; that is, some teams seemed unable to explain or justify their responses to items on the team readiness assurance test (tRAT). Following this observation, the authors conducted 4 analyses. Their analyses demonstrate how LMS-generated and recorded data can be used in a systematic manner to investigate issues in the real educational environment. The first analysis identified a team that performed significantly poorer on the tRAT. A subsequent analysis investigated whether the weaker team's poorer performance was consistent over a whole module. Findings revealed that the weaker team performed poorer on the majority of the TBL sessions. Further investigation using LMS data showed that the weaker performance was due to the lack of preparation of one individual team member (rather than a collective poor tRAT performance). Using the findings obtained from this case study, the authors hope to convey how LMS data are powerful and may form the basis of evidence-based educational decision making.