TEAM FORMATION IN ENGINEERING DESIGN COURSES
Bibliographic record
Abstract
Engineering design courses often include a team-based project. Project-based learning offers a great opportunity for engineering students to learn about teamwork and collaboration. It also gives students a chance to learn about themselves and improve their conflict management skills. Choosing the right team members for a specific project is not trivial, as the choice of the team often affects the project outcome and the students’ experience in the course. Moreover, there is a debate among engineering educators as to whether it is better to force team composition or not. In this paper, we investigate the impact of team composition and formation on project outcomes and student satisfaction in a second-year engineering design course at the University of Ottawa. The course is open to all engineering students and has an accessibility theme. Students work in teams with a client that has a specific accessibility need. Students meet the client three times during the semester and deliver a physical prototype by the end of the semester. For this study, students in the design course were divided into two groups. Students in the first group were allowed to pick their teams, while the instructor created the teams in the second group based on multidisciplinary composition and year of study. Both groups had the same instructor and the same course material, labs, project choices, etc. Semi-structured interviews were conducted with a few teams in each group.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".