Team-Based Learning Increases Active Engagement and Enhances Development of Teamwork and Communication Skills in a First-Year Course for Veterinary and Animal Science Undergraduates
Bibliographic record
Abstract
Team-based learning (TBL) was implemented into a first-year course (Principles in Animal Behaviour, Welfare and Ethics) for BSc Veterinary Bioscience (VB) and Animal Science (AS) students. TBL is now used widely in teaching medical students, but has had more limited uptake in veterinary education. This study reports its use over 2 years with cohorts of 126 and 138 students in 2011 and 2012, respectively. Average individual marks for multiple-choice question (MCQ) tests in the Readiness Assurance component of TBL were higher for the teams than for individuals for each session, explicitly demonstrating the advantages of teamwork. Students reported that they felt actively involved and that TBL helped them both with their learning and in developing other important skills, such as teamwork and communication. Qualitative analysis of written feedback from the students revealed positive themes of discussion, application, revelation, socializing, engagement, clarification, and retention/revision. In 2011 negative comments included the need to shorten the TBL sessions, but in 2012 tightening of the timelines meant that this was no longer a major concern. Requests to provide better introductory and background materials and ambiguity in questions in the TBL activities were what students least liked about the TBL. However, most comments were positive rather than negative in nature, and many students preferred the TBL to lectures. With requirements for curricula to teach professional skills, such as communication and teamwork, and the positive results from TBL's implementation, it is hoped that this study will encourage others to trial the use of TBL in veterinary education.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".