Addressing Infrastructure for Engineering Education in Africa
Bibliographic record
Abstract
Engineering education in African Higher Education Institutions (HEIs) has been widely criticized for poor infrastructure, which significantly undermines the quality of graduate engineers produced. The deteriorating state of facilities poses a major barrier to effective teaching and learning. This study adopts a socio-cultural perspective to investigate evidence of infrastructural decay in African HEIs offering engineering training and explores how other institutions globally have addressed similar challenges. Using a qualitative, desk-based approach, the study applies the Technological Determinism (TD) framework divided into processes, people, structures, and culture to analyze literature and propose solutions. Findings will lead to a proposed technological transformation model, aligning TD components with key institutional reforms: structures as disruptive innovations, people as HEIs, processes as indigenization of engineering, and culture as engineering culture. The study concludes that transforming engineering infrastructure through a synergy of these interdependent components is crucial for improving educational quality and student outcomes across Africa's HEIs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".