Will I fit? The impact of social and identity determinants on teamwork in engineering education
Bibliographic record
Abstract
In engineering as with many STEM spaces, the environment delivers many cues that affect psychological fit, which affects choices students make. Teamwork experiences can be particularly challenging for equity-deserving students. Using focus groups at a medium-sized multi-cultural Canadian university, we examined how engineering students navigated and experienced teamwork and how that interacted with social determinants (e.g., money and time constraints) and identity, including gender, race, and sexuality. We used the framework of State Authenticity as Fit to Environment to develop themes of teamwork choices, experiences, and outcomes. Social fit (respect from peers) and self-concept fit (whether self-image matches stereotype) affected many choices and experiences including selection of teammates with similar identities or allies. Women and low socio-economic status students sought self-concept fit by avoiding coding within teams. Visibly under-represented students felt pressure to excel to validate self-concept fit. The team environment itself sent messages about social and self-concept fit to many students, though the focus on collaboration and applications with social benefits often aligned with goal fit. These fit-guided choices and threats to fit nudged many students away from engineering careers. Interventions to address factors that cause negative experiences for marginalized students include strategic group composition, supporting mentorship and affinity groups, rotating group roles, structured collaboration, inclusive teamwork training and increasing diversity.
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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 | 0.000 | 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 | 0.000 | 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".