Componential Theories of Creativity: A Case Study of Teaching Creative Problem Solving
Bibliographic record
Abstract
Engineering is the discipline of applying scientific and mathematical tools to solve practical problems for society. At the core of a person’s problem-solving abilities is their creativity. This is a preliminary and exploratory theory-based paper summarizing the two most prevalent componential theories of creativity as applied to a case study. These theories outline a set of processes which contribute to a person’s ability to be creative in a domain. The components differ slightly between models, but include: motivation; domain-specific knowledge, skills, and abilities; and cognitive process of creativity including problem finding, ideation, and evaluation.To demonstrate the practical application of these theories to engineering pedagogy, they will be applied to a case study of a 2-day academic hackathon called “Tron Days”. Tron Days guides students through a multi-step modelling and verification process and concludes with teams of students designing and constructing a robotic arm. At the end of the second day, students demonstrated their functioning robotic prototypes. This event has now been run twice for first semester Mechatronics Engineering students, and similar implementations with different problems have been run in seven other engineering programs at the same institution. Each section of this paper will demonstrate the application of componential theories of creativity by drawing connections to the Tron Days event.
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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 | 0.011 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.012 | 0.009 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.006 | 0.006 |
| 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".