Session 36: Student perspectives on gender diversity in the classroom and implications for student recruitment
Bibliographic record
Abstract
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.018 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.115 | 0.054 |
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".