Trying Team-Based Learning in Two Classrooms
Bibliographic record
Abstract
Team-based learning (TBL) was originally designed in the context of business education but has proven to be a remarkably adaptable approach. Here, a statistician and an anthropologist compare notes on how they have used TBL in two of their courses, a third-year statistics course and a first-year anthropology course. Their motivations for adopting aspects of TBL differed, as did their methods for researching its effectiveness. In this article, we tack back and forth between accounts of why we chose to use TBL, how we adapted TBL to serve our purposes, and how we researched TBL in our respective classrooms. Ultimately, our contrasting experiences demonstrated the flexibility of team-based learning as a way to increase student engagement, accountability, connection, and success, and the importance of allies in supporting innovation in teaching and learning.
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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 | 0.017 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.016 |
| Scholarly communication | 0.014 | 0.009 |
| Open science | 0.004 | 0.017 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".