Building and evaluating team-based competencies: Closing the loop
Bibliographic record
Abstract
Team-based course and project work has become common in business and management education, and business schools are modifying their curriculum to include team-based activities to respond to desires of stakeholders. However, research and practice on how best to evaluate team-based competencies and use the information gathered to inform curriculum innovation in business schools is underdeveloped. In this paper, we shed light on key challenges associated with typical approaches (e.g., using students’ quantitative peer evaluations) used to inform program- and curriculum-level assessments of teamwork skills. To assist administrators, curriculum developers, and faculty alike, we offer a complementary approach that involves incorporating open-ended focus groups with students as an assessment tool for team based-competencies, as well as an informational tool for curriculum innovation. Our experience revealed that using these qualitative methods to examine team-based competencies offers new insights into curriculum and course development toward fostering teamwork skills, and providing students with more effective teamwork experiences.
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How this classification was reachedexpand
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".