A Comparison of Team-Based Learning Formats: Can We Minimize Stress While Maximizing Results?
Bibliographic record
Abstract
Team-Based Learning (TBL) is a collaborative teaching method in which students utilize course content to solvechallenging problems. A modified version of TBL is used at the University of Louisville School of Medicine.Students complete questions on the Individual Readiness Assurance Test (iRAT) then gather in pre-assigned groupsto retake the quiz, given time to utilize their learning resources and discuss each of the questions (Team ReadinessAssurance Test-tRAT). Following this discussion, students take an Individual Summative Assessment Test (iSAT)with new questions at a similar cognitive level and content focus. While educational gains of TBL have been shown,student evaluations negatively assessed the teaching method with complaints regarding question difficulty and stresslevels. Thus, during implementation of TBL in the School of Dentistry, three main changes were made: (1) Thecontribution of TBL to the overall grade was reduced (2) TBL questions were cognitively aligned with unit examquestions, and (3) Scratch-off, lottery response cards were used to create a fun, game-like environment. This revisedTBL format, compared to the original format, resulted in similar student performance during iRAT and tRATsessions. However, the revised, low-stress format had significantly higher scores on the iSAT (n=119-161, p <.05).Furthermore, students participating in the revised TBL format reported higher effectiveness of the learning format,higher levels of perceived fairness, and lower stress levels. These results suggest that the qualitative experience ofstudents may be an important consideration that should be carefully evaluated during implementation of a newteaching technique.
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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 | 0.007 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".