How we teach design: A comparison of Canadian and American courses.
Bibliographic record
Abstract
Chemical engineering design is a requirement for accreditation under the Washington Accord. Within that requirement there is variation in how chemical engineering design is taught and what is covered in programs in Canada and the United States. Chemical engineering design is somewhat unique with respect to engineering design in other disciplines as chemical engineering design is often taught at a system design level and focused on transforming raw materials into products (including energy products) and treating and/or recycling the waste materials [1]. Chemical engineering design encompasses the design of subsystems and components that are required for the plant or process design and the integration of subsystems into larger plant and/or societal systems [1,2] and in the context of a circular economy [2]. Due to this systemic and integrative perspective, chemical engineering design is uniquely positioned to support the UN Sustainable Development Goals (SDG)[2,3,4]. In 2022, the AIChE Education Division Curriculum Survey Committee conducted the capstone design course survey and received responses from ten Canadian universities and sixty-seven American universities and reported results [5]. In this paper, we compare American and Canadian programs based on the survey results and examine the similarities and differences based on geographic location and accrediting body among these programs.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.011 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".