Predicting Teamwork Performance in Collaborative Project-Based Learning
Bibliographic record
Abstract
Pulse of the Profession, published by Project Management Institutes (2017), reported that failed projects always lacked (a) clearly defined objectives to measure progress and (b) poor communication between team members. Minimizing communication costs and maximizing trust levels are essential to improve the efficiency of team performance. This study’s objectives required including how to formulate the problem and design the theoretical framework. The approach used involved a five-step team formation model with related definitions, including initial team forming, depending on group size, team agreement, role assignment, and team performance. The Predicting Teamwork Performance (PTPA) system was developed to help identify the functional roles of each member automatically. Role assignment provided a strong positive effect on team projects, while the role identification mechanism can assign team members responsibilities for some role(s) to enable learning. Self-assessment was used to identify team members’ strengths and weaknesses so that team leaders could easily recognize suitable types of roles for each member. Three primary team performance indicators—”Good”, “Pass” and “Marginal”—were reflected in the teamwork collaboration outcomes. The Predicting Teamwork Performance system reveals information about those outcomes through 1) individual performance indicator; 2) teamwork performance indicator; 3) personal skill sets results; 4) recommended skill sets improvements. The relationship between those indicators and practical roles was examined as analytical information for further project team formation.
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".