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Record W4403765024 · doi:10.24908/pceea.2023.17101

Assessing the Prevalence of Cross-cultural Competencies in Engineering Design Curricula: A Pilot Study

2024· article· en· W4403765024 on OpenAlexaffvenueabout
Zibo Zhang, Ada Hurst

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2024
Typearticle
Languageen
FieldEngineering
TopicEngineering Education and Curriculum Development
Canadian institutionsUniversity of WaterlooUniversity of Toronto
Fundersnot available
KeywordsCurriculumCross-culturalEngineeringMedical educationEngineering ethicsPsychologyMedicinePedagogySociologyAnthropology

Abstract

fetched live from OpenAlex

With the world becoming increasingly more globalized, cross-cultural competency has become essential for engineering design, especially in the Canadian context. The current paper presents a pilot study aiming to achieve two main objectives: 1) provide preliminary insights into the availability of cross-cultural concepts in engineering design curricula; 2) benchmark against design curricula in other design domains to identify ways that cross-cultural topics can be successfully integrated with engineering design education. The results reveal a general lack of cross-cultural concepts coverage in two sampled engineering programs. From an analysis of two benchmark design programs, three themes are identified that demonstrate how cross-cultural education can be delivered in design courses: 1) culture-related knowledge; 2) reflection on design topics using a cultural perspective; 3) collaboration with cross-cultural teams. The study aims to inspire engineering educators to incorporate cross-cultural concepts into their courses and curricula. This pilot study also helps validate the method for future larger-scale investigations.

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)0.001
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.534
Threshold uncertainty score0.752

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.018
GPT teacher head0.272
Teacher spread0.254 · 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 designObservational
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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