Trends of Women’s Participation in Engineering Education in the Republic of Benin and Implications for the Future of Higher Education
Bibliographic record
Abstract
Engineering, as well as men’s and women’s valuable labours or contributions, are important for the socioeconomic development of countries. This reality and the lack of data in this field in developing countries brought this paper’s authors to investigate the extent to which female and male students are enrolled and graduate in engineering education faculties in the Republic of Benin, a West African country. To this end, statistics of enrolment, graduation, failure, and exclusion of female and male students of the two oldest engineering education faculties, i.e., the Polytechnics School of Abomey-Calavi (EPAC) and the Faculty of Agricultural Sciences (FSA) of the University of Abomey-Calavi (UAC), have been estimated using Excel software and available enrolment, and academic results’ books and database. Pedagogical bylaws and other education policy documents were also reviewed for the sake of understanding the gender participation trends of the studied faculties. The analysis of almost four decades (1985–2022) of data revealed that very few (about 4,912, including 694 women) students got enrolled in the engineering programmes of the studied faculties. The total number of engineering students enrolled in the two faculties represents less than 1% of the total number of those who got their baccalaureate over the study period. Of the total number of women enrolled over the four decades, about 25% got excluded, while only about 22% of men got excluded at the polytechnic school EPAC. Meanwhile, at the Faculty of Agricultural Sciences FSA, 2% of the women enrolled were excluded against 1% of men. These results show that students are more excluded in the industrial engineering programmes of the polytechnic school compared to the agricultural engineering programmes of FSA. The main reasons identified for the small number of students enrolled in the engineering education faculties were, among others, the limited number of scholarships and places given to the engineering programmes by the government, donors and the faculties due to limitations in infrastructure and other resources available. With regards to the very poor participation of women in engineering programmes, socio-cultural stereotypes, poor social support or care provided to ladies and women, poor gender-responsiveness of STEM education and pedagogies, poor and late information on the advantages of engineering education and careers, sexual harassment, and early pregnancy, are few of the reasons mentioned by interviewees. More advocacy and more gender-responsiveness of further interventions might help improve the overall number of engineering students and the participation of women and other valid but less-represented people in engineering education programmes in universities of the Republic of Benin.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".