ENGINEERING EDUCATION FOR SUSTAINABLE CITIES IN AFRICA: A CASE FOR RWANDA
Bibliographic record
Abstract
Rwanda’s rates of urban growth and urbanization are unprecedented [1]. This change in the number of urban-dwellers will require increased engineering talent and resources to support resulting demands on the urban infrastructure. This study builds on the engineering education literature in Rwanda, explores how the country is prepared to manage current and future urban growth rates through engineering education, and examines the processes universities undergo when reforming their engineering curriculum in Rwanda. A research team travelled to Rwanda in July, 2016 to study engineering programs and interview two faculty members at two universities. Four significant subjects emerged from this study: the involvement of the political institutions in the curriculum design and approval processes, the need for allocating new resources to meet an increase in student enrolment, the importance of considering the historical and regional contexts in the curriculum, and the need for more hands-on training in engineering education. Further study is recommended on the political involvement in engineering curriculum reform, the government-led student and faculty funding initiatives, the impacts of the historical and regional contexts on the tertiary education system, and the increase
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.006 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".