Navigating the Complexities of Evaluating Team-Based Learning in the Graduate Classroom
Bibliographic record
Abstract
Team-based learning (TBL) appeals to public health educators because it mimics the real world of public health practice. Public health is an interdisciplinary field in which practitioners from various professional backgrounds come together to apply their different skills and competencies to a steadily changing array of public health problems. In addition to fostering synergistic learning, TBL can break down barriers between people from different professions and backgrounds. Many students have had past negative experiences with group work such as perceptions of unequal distribution of work and responsibility among team members. TBL extends beyond group work by supporting a pedagogical philosophy to empower students. Various methods of peer assessment have been proposed that embolden team members to evaluate one another’s contributions to group learning. We describe our TBL approach along with the strategies we employ to mitigate this particular challenge associated with TBL. Overall, we believe our approach to peer assessment in the context of TBL to be effective; students are more satisfied with the authentic assessment, and it has led to improved team functioning.
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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 | 0.109 | 0.228 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.013 | 0.005 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".