Gender differences in computation strategies: Evidence across adolescent and adult samples
Bibliographic record
Abstract
BACKGROUND: On computation items, young girls tend to use algorithmic approaches more than boys do. However, it is unclear whether these patterns persist as students progress into adulthood. AIMS: In two independent studies using different measures, we examine gender differences in computation strategy use in adolescents (Study 1) and adults (Study 2). We explore factors that might explain differences, and whether they relate to gender differences in math performance. SAMPLES: Study 1 uses data from students at a U.S. public high school (n = 213; 54.5% female). Study 2 uses data from U.S. adults (n = 810; 58.6% women). METHODS: Participants completed computation items, math performance measures and measures commonly found to relate to both gender and math. The unique relations between algorithm use, gender and math performance were examined while accounting for key covariates. RESULTS: Girls and women used an algorithm more often than their male counterparts, as did people with lower mental rotation skills and higher teacher-pleasing tendencies (Study 1) and higher test anxiety (Study 2). After including covariates, the gender difference in algorithm use decreased in Study 1 but not in Study 2. Across both studies, girls and women, and those who use algorithms more, had lower performance on problem-solving measures, as did those with higher teacher-pleasing tendencies and lower confidence (Study 1) and lower math anxiety (Study 2). CONCLUSIONS: Gendered patterns in algorithm use within older samples and the negative relation of algorithm use with math performance point to the need for renewed focus on developing children's computational approaches.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".