Team dynamics feedback for post-secondary student learning teams: introducing the “Bare CARE” assessment and report
Bibliographic record
Abstract
Team-based learning is recognized as an important opportunity for teamwork skill development, experiential learning, and learning from peers. However, team-based learning presents many challenges. One important challenge involves the accurate, reliable and valid assessment of team health. With such diagnostics, students could receive formative feedback and engage in meaningful goal setting, adjustment and action planning. Moreover, instructors could identify teams in distress and in need of support. We examine the team CARE assessment, which is a team diagnostic on ITPmetrics.com that assesses communication, adaptability, relationships and education/learning. Although the team CARE assessment has been used extensively in educational contexts, the assessment is lengthy and time-consuming to complete. We offer a shorter version, that we call the Bare CARE assessment, by utilizing psychometric analyses and content validity to eliminate the least useful items. We find that the Bare CARE is reliable and valid when considering correlations with teamwork variables and team performance (i.e. grades). Our sample of 61,549 students working in 14,601 teams offers state-of-the-art validation evidence using a large sample that ensures stable and robust results. We discuss the implications for use in pedagogy and future research.
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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 | 0.018 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".