Beyond the Numbers: An Intersectional Exploration of the Undergraduate Engineering Student Experience
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,009 | 0,011 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,003 | 0,003 |
| Études des sciences et des technologies | 0,023 | 0,018 |
| Communication savante | 0,014 | 0,010 |
| Science ouverte | 0,002 | 0,026 |
| Intégrité de la recherche | 0,003 | 0,007 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».