Session 36: Student perspectives on gender diversity in the classroom and implications for student recruitment
Notice bibliographique
Résumé
Session overview: Nationally, UK Higher Education (HE) appears relatively balanced in terms of gender, with 56.7% of undergraduates registering as female (HESA 2022-23 data). However, this balance is not reflected uniformly across subject areas. In Biological and Environmental Sciences (BES), 7 out of 9 programmes are significantly and persistently female dominated with some having as few as 8% males, despite being science-based programmes that are traditionally male-dominated. To better understand the issues related to recruitment of male students, focus groups were conducted with 121 students from across 8 programmes in BES. As part of this, students responded to short-answer questions concerning their choice of subject, motivations, and opinions on the importance of, reasons for, and ways to address, the student gender imbalance. The responses were then coded using a post-hoc code frame. Although 90% of students agreed that having balanced classes was beneficial, less than half were concerned about the imbalance and only a quarter said it should be addressed, as long as balance existed in HE generally. Predominantly, students chose their programmes due to love of the subject or related careers, and the imbalance was attributed to access and free choice being available to all and thus choices reflected inherent gender differences in interests or societal career pressures. As a result, many thought that strategies to recruit more males would have limited effect, but more minority representation on open days was suggested as the single biggest influence, followed by targeted advertising – including outreach talks at single-sex schools – and highlighting aspects of the programme that would appeal to the minority gender, as areas to prioritise. This study sheds light on student perceptions of gender balance and reinforces the recruitment strategies already in use. However, the student data raise the question of whether gender imbalanced student cohorts can, or even should, be addressed. Key learning points from this session: A better understanding of student choices and motivations when selecting a programme of study, and which recruitment methods students think are effective for improving diversity (specific interest for those who teach classes that are dominated by one gender, particularly if trying to improve the gender balance through recruitment - Athena Swan). Student perspectives on gender diversity in the classroom and implications for student recruitment PowerPoint. Only LJMU staff and students have access to this resource.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,018 | 0,016 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,008 | 0,002 |
| Communication savante | 0,006 | 0,004 |
| Science ouverte | 0,002 | 0,012 |
| Intégrité de la recherche | 0,008 | 0,008 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,115 | 0,054 |
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 source (Gemma direct ou Codex distillé), 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 ».