Gendered Decision-Making About Mathematics Courses: Contributions of Self-Perceptions, Domain-Perceptions, and Sociocultural Factors
Bibliographic record
Abstract
Girls continue to be underrepresented in Year 11 and 12 intermediate and advanced mathematics courses in Australia, which has implications for their future educational opportunities and career aspirations. The present study compared the choices of 84 Year 10 girls and boys from one school for their Year 11 mathematics course, with their teachers’ recommendations for the same. Findings indicated that while most participants made course selections aligned with their teachers’ recommendations, girls tended to under-aspire and boys tended to over-aspire in their choice decisions, based on their teachers’ recommended course choices. In addition, utilising the Expectancy-value theoretical (EVT) framework, we surveyed participants to measure their self-perceptions (self-concept), and values about mathematics (intrinsic value, utility value, and attainment value). We also measured participants’ views on the domain of mathematics (sense of belonging, growth mindset, the status of mathematics, gender bias). Multivariate analysis of variance indicated that girls showed lowered self-concept, sense of belonging, and growth mindset than boys, also viewing mathematics as less of a high-status subject than boys. In addition, the survey obtained participants’ opinions on sociocultural influences on their mathematics course selections, with no significant gender differences noted.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".