Beyond the Numbers: An Intersectional Exploration of the Undergraduate Engineering Student Experience
Bibliographic record
Abstract
The engineering profession seeks to diversify the people studying and practicing engineering. In Canada, diversity initiatives have focused on achieving gender parity for women, but progress has been slow. Little research exists that considers cultural systemic barriers, or the experiences of engineering students which could help explain why more progress to increase diversity in the engineering profession has not been made. This study explores inclusivity through the experiences of undergraduate students studying engineering at Ontario universities. Utilizing intersectionality as my framework and approach, I considered the complexities of students’ experiences through the dimensions of oppression and multiple identities. This mixed-methods study collected quantitative and qualitative responses from fifty-two undergraduate engineering students through an online survey and semi-structured one-on-one interviews with ten of the participants to answer the following research questions: how do students from marginalized groups describe their experiences in engineering education and how do those experiences vary for students of different genders, races, sexual orientations, disabilities, and socio-economic statuses? This study explored how identity impacted students’ experiences, whether students felt advantaged or disadvantaged by those experiences, and what students think their universities could do to provide a more inclusive learning environment. The key finding of this study was that students’ identities did impact their experiences in engineering education. Participants shared stories of marginalization and limited access to the whole engineering experience, meaning some students were underserved by their engineering programs and not fully engaged by the opportunities available to them. Women, queer students, racialized students, and students from lower socio-economic status described being excluded from the engineering student culture and community. Students from lower socio-economic backgrounds, students with disabilities, Black students, and women described experiences of othering, microaggressions, and discrimination. The stories and experiences shared by participants provides some evidence of a culture in engineering education that upholds maleness, whiteness, ableism, and classism. This finding implies that to be effective diversity initiatives need to address the culture and systemic socio-cultural constructs of power found in engineering education. Based on my findings and implications I provide recommendations for future research and outline strategies that engineering educators might utilize to make engineering more inclusive.
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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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.023 | 0.018 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.002 | 0.026 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".