A Narrative Study on First-generation Women Students’ Experiences and Persistence Decisions in Ontario Undergraduate Mathematics or Physics Programs
Bibliographic record
Abstract
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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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.006 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| 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".