A Comparison of Design Activity of Academics and Practitioners Using the FBS Ontology: A Case Study
Bibliographic record
Abstract
Abstract Academics teach engineering design based on design theory and best practices, practitioners teach design based on their experience. Is there a difference between them? There appears to be little prior work in comparing the design processes of design academics and practitioners. This paper presents a case study in which the design activity of a team of academics was compared to that of a team of practitioners. The participants’ verbalizations during team discussions were coded using the Function- Behaviour-Structure (FBS) ontology. A qualitative comparison reveals that the team of practitioners constructs the design space earlier and generally spends more time in the solution space than the team of academics. Further, the team of practitioners has a significant number of direct transitions from function (F) to structure (S), while no such transitions are observed for the team of academics. Given that this is a single case study, the results cannot be used as the basis for any generalizations on how academics and practitioners compare. This is a successful proof of methodologies that lay the foundation for a series of hypotheses to be tested in a future study.
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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 | 0.032 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".