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Record W2885301085 · doi:10.24908/pceea.v0i0.7371

CREATIVE TRANSFER IN THE ENGINEERING CLASSROOM

2017· article· en· W2885301085 on OpenAlexaffvenue
Ken Tallman, Christina Mei

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2017
Typearticle
Languageen
FieldEngineering
TopicBiomedical and Engineering Education
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsCreativityCertaintyMetacognitionPsychologyMathematics educationCurriculumUndergraduate researchPedagogyMedical educationCognitionSocial psychology

Abstract

fetched live from OpenAlex

This research on creativity in undergraduate engineering education asks whether undergraduate engineering students in a Fall 2016 course will develop enriched creative skills in other learning and professional environments as a result of having taken the course. The motivation for this study comes from the need for a clearer understanding of how and where to teach creativity in the undergraduate curriculum and a clearer understanding of how students transfer skills and knowledge from one setting to another. As well as studying students’ creative growth, the research will analyze students’ metacognitive development. What do students learn about how they learn by taking thiscourse? Is this knowledge valuable? Are students able to better articulate their creative processes once they have finished the course? Have they found ways to make use of this advanced knowledge? The results from this research are preliminary and inconclusive, but appear promising. Research data at present consists primarily of audiorecorded interviews with consenting students, and more data is likely required to provide better certainty about whether students have been able to transfer their creative activity from this course to other situations.

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 score0.595
Threshold uncertainty score0.662

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.006
GPT teacher head0.192
Teacher spread0.187 · 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

Citations1
Published2017
Admission routes2
Has abstractyes

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