Creativity in Design Engineers: Attitudes, Opinions and Potentially Influential Factors – Part II
Bibliographic record
Abstract
.Abstract – In the latter half of 2015, a survey looking at attitudes and beliefs about creativity was distributed on the campus of the University of Saskatchewan. Over 2000 responses were gathered, including more than 200 in the College of Engineering. Initial quantitative results from this study were reported in 2016 in Neufeld et al [2]. In terms of the methods used in the study, as discussed in Neufeld et al [2], an online pilot survey was distributed to students and faculty from a variety of the Colleges at the University. Survey questions probed respondents’ affinity for creativity, their personality characteristics, their opinions on state, trait and skill-based viewpoints on creativity, and demographic details. The first part of the survey was a validated Creative Attitudes and Values measurement tool (part of the Runco Creativity Assessment Battery (rCAB)© 2012), as discussed in Acar and Runco [1]. This tool consists of 25, 5-point Likert scale items. Of these 25 items, 15 and 10 were indicative and contraindicative items, respectively. Contraindicative items were reverse coded so that they could be used along with the indicative ones. Both past research and our results showed good inter-item reliability scores for this measurement tool. In Neufeld et al [2] we presented results covering all of the closed-form, quantitative questions along with some correlational calculations with the rCAB scores. The focus of the current paper is on the qualitative results, as well as on a factor analysis of the rCAB questions. The factor analysis was quite successful. We used SPSS and forced a correlation of items, reducing to three factors. We have just over 29% of variance accounted for, with 10% non-redundant residuals. We have strong anti-correlation between one factor and the other two, and no correlation between the other two. These results will be compared to those of the rCAB authors [3]. As for the qualitative data, we asked several open-ended questions to probe how respondents defined creativity, whether they regarded it as a positive behavior, as well as how they felt about creativity in terms of it being a skill, trait and/or state. For example, pairs of questions asked when creativity is difficult and easy, when it should and should not be used, and when it grows and diminishes. For each of the 9 questions that had open-ended answers, concepts were extracted from individual responses. Concepts were then grouped into themes. Themes and concepts were compared across questions and were aligned. Responses were then coded for concepts and themes. At this point, the text data could be quantitatively examined. This paper presents those results, and discusses the implications of the concepts, themes, and their statistics for how we talk about creativity, and how we can teach it. Comparisons will be made between the results from engineering students and staff versus non-engineers. This paper completes the first level of evaluation of the results of this initial survey focused on attitudes and beliefs about creativity. Future work will focus on examining correlations between the results of different questions, including the rCAB scores.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".