A Comparison of Team-Based Learning Formats: Can We Minimize Stress While Maximizing Results?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it