HOW DO FIRST YEAR STUDENTS FORM THEIR PROJECT TEAMS?
Bibliographic record
Abstract

 Abstract - Common wisdom of how students, form their teams for projects is "who they know", not necessarily who would make a good teammate, nor someone they can actually work with. In their first semester on campus, Mechatronics students have multiple opportunities to work with their classmates, any of which could have contributed to how they formed their course project team. The activities range in scope from straightforward assignments to challenging projects, and vary in length from one, to several weeks. This research was conducted as a sequential explanatory, mixed-methods study. First semester team formation data was cross-checked with survey responses, and student self-reporting on satisfaction with their choice of team members. Focus groups were then conducted to investigate external forces on team formation. Conclusions from the initial work show that students are much more strategic with who they work with than initially hypothesized and the motivations behind the choice of teammates are diverse, and complex. Further work needs to be completed to see how widespread these motivations are across Engineering at UWaterloo.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".