“The Girls and Math Problem” An Exploration of Middle School Girls’ Confidence in the Mathematics Classroom: A Teacher Perspective
Bibliographic record
Abstract
Women in mathematics has been a topic of discussion for several decades. In North America, it was observed that middle school aged girls display low interest and confidence in their ability to perform in the mathematics classroom regardless of their academic ability. This study seeks to determine the social factors as observed by teachers relating to the declining confidence and limited interest in math amongst girls, and to draw comparisons to the existing body of research for girls in middle school. This study explores teacher observations in their single gender and co-educational classrooms through qualitative research; semi-structured interviews. Findings indicate that, while ability is not an issue, confidence remains an observed problem. The influence of parents on girls’ confidence is strong, which can be more influential than those of peers in certain situations. Single gender mathematics classrooms can also be used to meet the different learning needs of boys and girls. Role models and on-going school-wide initiatives which promote mathematics as enjoyable and accessible can encourage girls and boys. Implications broadly focus on the systematic spread of this issue, given the history of gender equity research and the bias in which a teacher could bring to the classroom.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".