MEASURING THE INFLUENCE OF TEAM FUNCTIONING ON DESIGN PROJECT OUTCOMES
Bibliographic record
Abstract
The influence of team function on designproject outcomes was examined in this study. Teamfunction was considered across six key dimensions,including unity, communication, distribution ofresponsibility, problem solving, conflict management, andteam self-evaluation. Three different methods were usedto quantify team function: a survey in which students selfratedtheir team’s function, a comparison of performanceon quizzes first performed individually and then as a team(as a measure of the degree of communication, problemsolving, and unity), and an analysis of the differentiationin inter-team peer evaluation scores (as a measure ofdistribution of responsibility, conflict management, andunity). Design project outcomes were measured as acomposite of grades from competition prototypes, writtenreports, oral and poster presentations, and several otherdeliverables. These scores were normalized to removeyear-to-year variability. Statistically significantrelationships between each measure of team function anddesign project outcomes were observed.For each dimension of team function, teams with highaverage self-rating on the survey also had 4% to 6%higher normalized design project scores compared tothose with low self-ratings. On the quizzes, teams thatwere more likely to answer a question incorrectly whenone or more members knew the correct answer(suggesting a lack of communication, unequal input toproblem solving, or reduced team unity) also receivedlower normalized design project scores by as much as4%. The full relationship between this metric and projectoutcome was more complicated though, as the teams leastlikely to answer incorrectly when some members had thecorrect answer performed below average on the projects.Lastly, a trend of decreasing composite project score wascorrelated with increasing inter-team differences in peerevaluation scores (suggesting unequal distribution ofresponsibility, increased conflict, or reduced team unity).Interestingly, teams that did not differentiate peerevaluation scores at all (i.e. each team member receivedthe same peer evaluation score ‘no matter what’) hadproject scores 7% lower on average than teams with asmall non-zero differentiation in peer evaluation scores.Taken together, the results of this study support thehypothesis that team function plays an important role inproject outcomes, contributing better than half a lettergrade difference.
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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 | 0.019 | 0.087 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".