Team dynamics feedback for post-secondary student learning teams
Bibliographic record
Abstract
In the current research, we introduce the team CARE model for supporting team development during post-secondary education. Team CARE is part of a larger suite of assessments at itpmetrics.com. Team CARE is a free, online, survey-based assessment that allows team members to rate their team’s health and functioning in four key categories (communicate, adapt, relate and educate), as well as provide written feedback about the team’s functioning to add nuance and supplemental context to the numeric scores. Team members completing the assessment receive a report documenting their team’s scores on the variables measured. We report on data from student learning teams suggesting that the variables in the team CARE model are reliable, and that they are correlated with team performance outcomes. Students’ perceptions of the tool were also examined, and the findings suggest that team CARE is perceived to be valuable, useful and easy to use. Recommendations for practice are detailed, including sample assessment schedules for teams with differing life spans. Implications for future research and implementation are discussed.
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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 | 0.010 | 0.075 |
| 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 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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".