Gender Disparities in Academic Performance in the Sciences
Bibliographic record
Abstract
The gender disparity in numbers of students attending STEM programs has been a challenge since science began. But regardless of this disparity in numbers, differences of gender in academic performances have also been reported, including average GPA, performances during exams and in‐class participation. The majority of these performance differences negatively impact women, potentially affecting retention, professional development and professional confidence, further impacting gender equity in the sciences. To analyze whether such disparities exist at the University of Ottawa, we have collated a dataset of the academic performance of all students registered at the University of Ottawa in the Faculty of Science between 2014 and 2019. This includes all students registered in the departments of Biology, Chemistry and Biomolecular Science, Earth and Environmental Science, Mathematics and Statistics and Physics, which represent over 4,000 students per year. The students’ cumulative GPA was used as a covariate to control for their prior academic ability. Initially, when combining all 122 classes included in this study, analysis of covariance and multi‐covariate analysis of variance revealed no gender performance differences. However, when classes were analyzed individually, a gender performance difference was observed in 18 classes (p‐value<0.05), and male students were favored in 83.33% of classes with a gender performance difference. The language of study (p‐value of 0.001965) and class size (p‐value of 0.03598) were factors associated with gender differences in academic performances. Classes which numbered more than 100 students were more likely to be less favourable to women and typically large enrolment 1 st and 2 nd year classes, regardless of the class subject, represented 14 of the 18 classes with gender differences in academic performances, all of which were more favourable to males than females. No 4 th year classes had a gender bias and, curiously, of the only four 3 rd year classes with a gendered difference in academic performance, 3 were more favourable to females than to males. Understanding the impact of gender, language and class size on our student population and their influence on academic performance will help us develop actionable measures to improve education outcomes for all our students. However, a deeper understanding of contextual conditions leading to gender disparities in academic performance are needed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".