A qualitative assessment of barriers within the university learning environment and their influence on students’ participation in engineering education
Bibliographic record
Abstract
Abstract The field of engineering is essential for socio-economic development; however, it is characterised by low female representation. The objective of this study was to explore the challenges within the university learning environment that could affect the participation of women and men in engineering and propose recommendations. Six focus group discussions (FGDs) were held with undergraduate students from a Ghanaian university (i.e. one FGD each for male and female students in the second, third, and fourth years of study). The discussions, among others, elicited the challenges that students face during their studies, their coping strategies as well as some pointers for enhancing students’ experiences in the engineering learning environment. Data were analysed by employing content analysis with the aid of Atlas.ti software and then categorised into themes inspired by the three dimensions of learning environment. Barriers related to the physical learning environment were overcrowded lecture rooms and inadequate laboratory space and equipment. Barriers related to pedagogical learning environment were inadequate practical sessions, outmoded curriculum and poor teaching methods, and limited orientation for students. Barriers associated with the psychosocial learning environment included inadequate involvement of females in practical work, backlash and apathetic attitude when women become group leaders, silencing and intimidating female students in class, and limited female role models. These barriers combined with gender stereotypes exhibited by male students, laboratory technicians and lecturers intimidate female students, reduce their confidence levels and limit their exploratory abilities. We recommend sensitising and training lecturers and students to mainstream gender considerations in learning environments to make engineering gender-neutral. It is also critical for engineering faculties to develop and implement practical-oriented and gender-responsive curricula and pedagogies, standardise teaching methods, and explore e-learning options to compensate for the increasing number of students in enrolment. Universities must also devise intentional strategies for recruiting and retaining more female engineering lecturers and enhancing the quantity and quality of teaching and learning infrastructure.
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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 | 0.015 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".