Creativity in Design Engineers: Attitudes, Opinions and Potentially Influential Factors – Part II
Notice bibliographique
Résumé
.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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,006 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».