Creativity in Design Engineers: Attitudes, Opinions and Potentially Influential Factors
Bibliographic record
Abstract
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 distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".