Effect of personality traits in team dynamics and project outcomes in engineering design
Bibliographic record
Abstract
The University of Ottawa’s Faculty of Engineering is home to multiple rapid prototyping facilities and entrepreneurship spaces.These include a makerspace, a machine shop and a design space for any student to use free of charge. In the Makerlab, studentstake courses that introduce them to collaborative project-based learning, engineering problem-solving and prototyping. The goal ofthe first- and second-year engineering design courses is to introduce engineering design processes, time and project management,and analysis, prototyping and testing. In each course, students work in groups on a semester-long project to meet the needs of areal client whom they meet with three times over the course of the project. The objective of this paper is to understand the impactof each team member’s personality, more specifically the Big Five personality traits (openness, conscientiousness, extraversion,agreeableness and neuroticism), on team dynamics and team performance with regards to their project throughout the semester in aproject-based learning environment. Factors considered are gender, GPA, the Big Five personality scores, final peer evaluationsand team dynamics, project manager evaluations and project grades. Multiple regression analysis is conducted to determine if anyof the factors listed influence team performance and dynamics as well as individual project grades.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".