Connection, Engagement, and Belonging: Exploring Young Women’s Positive Experiences for Building Inclusive STEM Classrooms
Bibliographic record
Abstract
This qualitative study was designed from an appreciative and positive research focus to examine how and why young women engage in, succeed, and persevere in STEM courses. The objective was to gain student perspectives on improving gender equality in STEM education. From the questionnaire and focus groups with participating women university students enrolled in STEM courses three themes emerged (a) Relational Connection with teachers and/or students, (b) Engagement with STEM Curriculum that reflected influential pedagogical learning cultures, and (c) Cultures of Belonging and inclusion. This research offers insight into positive factors for women’s success in STEM academics and careers. Keywords: Female students in STEM; STEM Education; STEM Engagement; Inclusivity; Gender Bias Cette étude qualitative a été conçue dans une optique de recherche appréciative et positive afin d'examiner comment et pourquoi les jeunes femmes s'engagent, réussissent et persévèrent dans les cours de science, technologie, ingénierie et mathématiques (STIM). L'objectif était d'obtenir le point de vue des étudiantes sur l'amélioration de l'égalité des sexes dans l'enseignement des STIM. Le questionnaire et les groupes de discussion auxquels ont participé des étudiantes universitaires inscrites à des cours de STIM ont permis de dégager trois thèmes : (a) le lien relationnel avec les enseignants et/ou les étudiants, (b) l'engagement dans le programme de STIM qui reflète des cultures d'apprentissage pédagogiques influentes, et (c) les cultures d'appartenance et d'inclusion. Cette recherche offre un aperçu des facteurs positifs pour la réussite des femmes dans les études et les carrières en STIM. Mots clés : étudiantes dans les STIM ; éducation aux STIM ; engagement dans les STIM ; inclusion ; préjugés sexistes
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.009 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".