The Experiences of Women in Undergraduate Engineering
Bibliographic record
Abstract
While it is understood that there is an issue in retention of women in STEM fields, there is little research addressing why this is occurring. This warrants the need to begin identifying the root of this issue, and the direct causes. Undergraduate engineering programs are the first phase students undertake in their pursuit of a career as a professional engineer, and even in this early phase an issue in retention is apparent. The present study addresses factors that may be preventing female students from remaining in the field. In this study, 261 undergraduate students enrolled in engineering programs at an accredited university in Ontario completed a 17-question survey that collected demographic data and asked questions related to challenges students may experience during their education. The survey questions were formulated to understand the severity to which each challenge affects males and females in different ways. Demographic factors were correlated with students’ answers, and significant differences between men’s and women’s answers were found. 65% of female students reported more discouragement or intimidation by peers and professors in their program, compared to 45% of male students. These findings may indicate that female students may be more susceptible to a culture of intimidation than male students. Another significant finding lies within a survey question formulated to understand if female and male engineers have their contributions represented equally. This question showed that male students recognized the names of male engineers 1.75 times more often than they recognized the names of female engineers. The results of this question show that, to some capacity, male engineering students do not recognize female engineers’ contributions as frequently as male engineers’ contributions. Moving forward, there is a need to support female students in engineering as well as to educate students on respect and equity.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.008 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".