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Record W4405675634 · doi:10.24908/pceea.2024.18529

How we teach design: A comparison of Canadian and American courses.

2024· article· en· W4405675634 on OpenAlexaffvenueabout
Marnie Jamieson, Laura Ford

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2024
Typearticle
Languageen
FieldEngineering
TopicDesign Education and Practice
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMathematics educationSociologyEngineering ethicsPsychologyEngineering

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.448
Threshold uncertainty score0.929

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.016
GPT teacher head0.244
Teacher spread0.227 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes3
Has abstractyes

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