Barriers to women’s participation in higher engineering education: a qualitative assessment of the role of social networks of students in a Ghanaian university
Bibliographic record
Abstract
Abstract Engineering is critical for socio-economic development, however only a few women participate in engineering education and careers. This study aimed to identify the types of negative information propagated by the social networks of engineering students that could create barriers to students, and particularly women’s retention in engineering education and careers, and assess whether they influence men and women differently. The study was exploratory hence six focus group discussions were conducted with undergraduate engineering students in their second, third, and fourth years of study in a Ghanaian university. An interview guide was used to, among others, examine the perceptions and misconceptions of students’ social networks about engineering and the negative information that circulates within the networks. Demotivating information from students’ social networks were mainly misconceptions such as (1) engineering is too difficult and strenuous for women, and only meant for strong and well-built people, (2) engineering negatively affects women’s beauty and body image, (3) engineering makes women unfashionable and unattractive, and (4) engineering is a threat to marital and family lives. Both female and male students were negatively affected by misinformation about engineering being difficult and having limited job prospects as well as societal preferences for other programmes such as medicine. The misinformation could serve as a barrier, especially for students lacking ‘faith and the spirit of perseverance’ to pursue and graduate from engineering programmes. While efforts are being made to bridge the gender gap in engineering education and careers, there are misconceptions and misinformation that can hinder progress toward achieving the desired gender parity. Educational policies must integrate gender-responsive strategies including addressing the socio-cultural and stereotypical factors and public misconceptions. There is a need to provide strategic counselling services to engineering students to be able to cope with the effects of negative information from their social networks especially during the early years of their studies in the universities.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".