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Record W2604240173 · doi:10.24908/pceea.v0i0.6519

Creativity in Design Engineers: Attitudes, Opinions and Potentially Influential Factors

2017· article· en· W2604240173 on OpenAlexafffundvenueabout
Paul Neufeld, Omid Mirzaei, Mark A. Runco, Sean Maw

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2017
Typearticle
Languageen
FieldPsychology
TopicCreativity in Education and Neuroscience
Canadian institutionsUniversity of Saskatchewan
FundersUniversity of Saskatchewan
KeywordsCreativityRespondentPsychologyMathematics educationCurriculumVariety (cybernetics)PedagogyComputer scienceSocial psychologyPolitical scienceArtificial intelligence

Abstract

fetched live from OpenAlex

Is creativity important in engineering design? If it is, then why do most undergraduate engineering programs spend so little time teaching creativity? And therefore, as a result of our programs, do our students emerge more creative, less creative or no different compared to when they arrived? If creativity is worth developing, can we accurately measure it in our students, and can we enhance it systematically?These were some of the questions that motivated the initiation of a creativity research program in the College of Engineering at the University of Saskatchewan. The assumption was that creativity is important in engineering, especially in design. The intent was to understand how we could assess creativity in our students and then enhance it.The focus of this initial study is a precursor to many of these more applied questions. We had students and faculty from a variety of Colleges, including Engineering, answer an online survey that probed attitudes towards creativity, respondent personality characteristics, opinions regarding conditional influences on creativity, and potential demographic factors influencing the creativity of individuals. As well, we employed a validated creativity attitudes and beliefs measurement tool (rCAB) as an accepted benchmark for assessment.The survey included both closed- and open-ended questions. The results from some of the open-ended questions have been analyzed to determine emerging groups of similar types of answers, and then efforts have been made to relate the groups in a meaningful framework.The results for the Engineering students are emphasized, but they are also compared with students and faculty from other Colleges. Closed questions were analyzed using inferential statistical tests (distributions, means, standard deviations, t-tests, ANOVA, Cronbach’s alpha), while the open-ended responses are compared more qualitatively when they cannot be quantified easily.The survey went through ethics approval and was distributed in the latter half of the Fall 2015 term.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.010
metaresearch head score (Gemma)0.025
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.054

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.025
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0020.002
Scholarly communication0.0040.001
Open science0.0000.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.026
GPT teacher head0.304
Teacher spread0.277 · 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 source (direct Gemma or distilled Codex), 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

Citations4
Published2017
Admission routes4
Has abstractyes

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