Experiential Learning vs Systematic Prescriptions in Engineering Design: A Crossroads for Education
Bibliographic record
Abstract
The systematic, non-experiential prescriptions of classical design methodology continue to have a strong presence in large segments of design research and education while another segment sees domain experience and consequent intuition and creativity as being key to successful design. In this paper the two approaches are outlined and the empirical research literature in human behaviour is employed to discern discrepancies and potential weaknesses. Results show that gaining experience in a domain intrinsically changes how one designs, which the classical methodology does not account for. For example, only designers with tactile and visual domain experience can abstract functions per the dictates of the classical (non-experiential) methodology, which means that they cannot have used the methodology to learn basic design in the first place – or did so only with great difficulty. This and other conflicts pose problems for the education of engineering design students, and to fathom their extent this paper surveys engineering design textbooks offered in Canada and the U. S.; all of the books are found to embrace the classical methodology. If they are to remain involved in preparing students for entry into industry then some aspects of their contained classical methodology must be supplanted by experiential approaches to design educatio
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 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 | 0.020 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.034 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| 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".