A measured educational experience developing creativity with graduate and undergraduate engineering students
Bibliographic record
Abstract
Creative and innovative people are recognized for their contribution to society’s wellbeing. Engineers are often called upon to produce innovative ideas and thus participate in the improvement of their organization’s products, services, and processes. Not aiming at educating specialists with a degree in creativity, we nonetheless believe future engineers could benefit from a deliberate development of their creative abilities. However, building an effective and valid course to develop participants’ creativity is not without challenges.This paper describes an innovative cognitive approach to enhance creativity as well as the pedagogical strategies underlining a creativity course for engineering students. It also presents the pre-post results measured with a revised version of the CEDA (Creative Engineering Design Assessment). We evaluated the creative performance of 59 students before and after a 45-hour creativity course. Quantitative data shows statistically higher numbers after the course than before. Qualitative data provides further evidence of the course’s relevance and effectiveness.We conclude that students’ creativity can be increase and that the course enables a better understanding of creativity and how to foster it.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".