A Narrative Study on First-generation Women Students’ Experiences and Persistence Decisions in Ontario Undergraduate Mathematics or Physics Programs
Notice bibliographique
Résumé
Though participation and persistence rates for women in university science, technology, engineering, and mathematics (STEM) programs are increasing, the experiences of an equity-deserving group, first-generation women students (FGWSs), those women first in their family to attend college or university, are not widely understood. This study used narrative inquiry and interviews to explore the journey of 12 FGWSs into and through their mathematics and physics programs, how they understood and made sense of their experiences, and how these experiences shaped their persistence decisions. The conceptual framework guiding this study draws upon Terenzi and Reason’s (2005) persistence framework, De Grandi et al.’s (2019) STEM climate constructs, and STEM literature. Although findings are mostly consistent with previous research, they also reveal new insights into FGWSs’ challenges and successes and how they use different lenses (e.g., gender or first-generation status) to understand their experiences. Growing up, participants developed an early interest in STEM through informal exploration and teachers who fostered their passion. Once they began university, participants felt disoriented as they were unfamiliar with the university context. Several themes emerged relating to their university journey. First, first-generation status is a hidden component of social identity acknowledged during first year. They do not know what they do not know (or should know), and some had experiences where they felt at a disadvantage. However, they were determined and recognized that they could ask their peers for advice. Second, doubts about their abilities led to lower academic confidence, imposter feelings, and thoughts about switching programs. However, they adopted new perspectives on learning and success and persisted as they felt a sense of pride in their accomplishments. Third, most participants suggested that STEM professors should adopt a pedagogy of care. They felt that student-centred teaching approaches engaged them and made them comfortable approaching professors. Lastly, even though their families did not have knowledge about university, many felt that family support was influential in their persistence. Not all FGWSs have the same experiences in mathematics or physics programs, so a one-size-fits-all model to support them is not appropriate. Several recommendations for post-secondary institutions and STEM departments are provided.
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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 | 0,004 | 0,006 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,013 | 0,006 |
| Communication savante | 0,004 | 0,004 |
| Science ouverte | 0,001 | 0,005 |
| Intégrité de la recherche | 0,001 | 0,003 |
| 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 ».