Engineering Education for Sustainable Cities in Africa: Conversations From Kenya
Bibliographic record
Abstract
Projected urban growth in Kenya will require increased engineering talent and resources to facilitate the infrastructure demands of growing urban centers [1]. This study aims to understand how Kenyan institutions are currently equipped to support engineering programs and what factors need to be considered to reform the curricula to support rapid urban growth in the region. To investigate these questions, a research team travelled to Kenya in July, 2016 to study the engineering programs and interview faculty members at two universities. The method of currere was used as a framework for analyzing the regressive, the progressive, the analytical and the synthetical. Four key insights emerged from the study: opportunities exist for curriculum reform of Kenyan engineering curricula to mirror emerging engineering pedagogy and practice, context is a critical factor in curriculum reform, accreditation is a key consideration for online learning in engineering education, and there was an openness to collaboration regarding curriculum reform. This study focused on the perspective of the educational institution. As such, further research is suggested regarding the institutional factors, notably the accreditation standards for online learning, the industry objectives and the policy objectives to inform the context of the conversations of engineering education in Kenya.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".