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Record W3003011250 · doi:10.24908/pceea.vi0.13752

FROM STEM TO STEAM IN ENGINEERING DESIGN

2019· article· en· W3003011250 on OpenAlexafffundvenueabout
Chantal Rodier, Mohamed Galaleldin, Justine Boudreau, Hanan Anis

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2019
Typearticle
Languageen
FieldEngineering
TopicDesign Education and Practice
Canadian institutionsUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsThe artsInternshipCurriculumCreativityEngineering ethicsEngineering educationEngineeringPedagogyEngineering managementSociologyPsychologyPolitical scienceMedical educationMedicine

Abstract

fetched live from OpenAlex

Creativity, communication skills, interdisciplinary sensitivity, and cultural and civic responsibility are vital skills and perspectives to inculcate in contemporary engineering students. A number of studies have demonstrated the benefits of exposing engineering students to arts, as studying arts and humanities can open up their minds to creative ideas from great minds outside of science and engineering. In most cases, engineering students are exposed to the arts by taking a few non-technical courses as electives. Many students view these courses as less important and irrelevant to their field of studies. Integrating the arts into the technical engineering curriculum is challenging but critical to engineering design, particularly in early years, and represents a natural opportunity. This paper discusses the approach taken by the Faculty of Engineering at the University of Ottawa of exposing students to the arts through curricular and extra-curricular design activities. These include offering design challenges, a first-year engineering design course and summer internships. This paper also discusses the challenges that arise in delivering such curriculum and the impact of such exposure on the engineering students involved.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.520
Threshold uncertainty score0.832

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.007
GPT teacher head0.192
Teacher spread0.184 · 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 designSimulation or modeling
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

Citations3
Published2019
Admission routes4
Has abstractyes

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