“I Fall Asleep in Class … But Physics Is Fascinating”: The Use of Large-Scale Longitudinal Data to Explore the Educational Experiences of Aspiring Girls in Mathematics and Physics
Bibliographic record
Abstract
This article explores how students' aspirations to study mathematics or physics in post-16 education are associated with their perceptions of their education, their motivations, and the support they feel they received. The analysis is based on the responses of around 10,000 students in England in Year 8 (age 12–13) and then in Year 10 (age 14–15). The students were first surveyed during 2008–2009 and then followed up in 2010–2011. t-tests revealed a decline in their perceptions of their mathematics and physics education. Factor analyses indicated subject-specific constructs that were associated with gender aspiration groups (i.e., high-aspiring girls, high-aspiring boys, low-aspiring girls, low-aspiring boys). High-aspiring girls were more likely than low-aspiring boys to be positive about mathematics/physics education, motivation in these subjects, and support received. However, high-aspiring girls were less likely than high-aspiring boys to be encouraged by their teachers and families to continue with these subjects post-16 and had lower self-concepts, intrinsic valuations, and perceptions of lessons. Low-aspiring girls reported the least favorable views of their mathematics/physics education of all four gender aspiration groups. Findings were generally similar for mathematics and physics, although students overall responded more favorably to mathematics than to physics. The quantitative findings are illustrated with extracts from longitudinal interviews (ages 15, 16, and 17) of two high-aspiring girls.
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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 | 0.009 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".