A SURVEY OF PSYCHOLOGICAL TRAITS BEHIND LEARNING TO DESIGN: NOVICES AS DORMANT EXPERT DESIGNERS
Bibliographic record
Abstract
With their increasing emphasis on the importance of hands-on practice and gaining experience, the fields of engineering-design research and education appear to be entering a human-focused transition other fields like economics and decision making have emerged from in the recent past. In addition to the original, modernism-rooted desire for a rational science of design modelled on the natural sciences, this delay may be due the inherently strong association of engineering with science – i.e., ‘applied science.’ This research investigated whether there may instead be a science to the human involvement in design, of the human behaviours that often appear in actual engineering design practice. It surveyed published empirical studies in psychology, child development and other social and life sciences – as well as those within design research itself. Of particular interest were the designer behaviours and activities which did not follow the prescriptions of – or were prescribed against by – the traditional, rational-design methods: visualization, single-solution conjecturing, and intuition. Results from this survey showed comprehensively that environmental interactions and authentic design experiences activate latent design abilities and coping mechanisms that may be difficult to obtain otherwise. Without such interaction and the gaining of experience there can be no designing, so essentially design is a wholly human phenomenon. Rather than follow the rational-design method and prescribe against these design-enabling behaviours, then, it appears that a better pedagogical approach is to allow them to develop and mature – and let design novices become the experts they were meant to be.
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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 | 0.008 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".