Gender Disparities in Academic Performance in the Sciences
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».