Navigating the Complexities of Evaluating Team-Based Learning in the Graduate Classroom
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) 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.
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 | 0.019 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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