The influence of gender on collaborative projects in an engineering classroom
Bibliographic record
Abstract
Using a qualitative approach to data collection and analysis, the article discusses some of the findings from a larger study on collaboration and the role of gender. We profile three student engineering teams as they participate in processes leading to the submission of a report for a team-based technical communication course. While some theorists suggest that gender can play a significant role in achieving a successful team dynamic, our study only partially supports that claim. A synopsis of two women from two predominantly male teams reveals glimpses of what the literature describes as traditional gender-linked behaviors by both men and women, but the all-female team does not conform to stereotypical patterns and their behaviors call into question the existence of these interactional styles. We suggest that factors other than gender and independent of a team's gender composition exert a greater impact on collaboration. Nevertheless, the study does caution against assigning women to predominantly male teams, since when a team's social structure is mostly male, traditional gender-linked interactional behaviors as well as manifestations of the culture of engineering are more likely to emerge. Overall, the study underlines the importance of examining specific face-to-face interactions to see how behavior is situationally produced in order to more fully understand the interactional strategies open to individuals.
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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 | 0.011 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 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".