A NEW APPROACH FOR PROTOCOL ANALYSIS ON DESIGN ACTIVITIES USING AXIOMATIC THEORY OF DESIGN MODELING
Bibliographic record
Abstract
Although various design methodologies have been developed to help designers generate design concepts and design ideas, they must be applied by designers. Designers play an important and critical role in delivering a successful and innovative design. To understand how designers think in solving a problem during the design process will help us develop a new design methodology that can accommodate designer’s performance. This paper proposes a new protocol analysis approach to studying designer’s cognitive behaviour during the design process by using the axiomatic theory of design modeling. The conventional protocol analysis approach, which includes transcripts, segmentation, encoding and experiment results analysis, largely depends on the experience of the people who conduct the analysis. In this research, the concept of design state, derived from the axiomatic theory of design modeling, is used to guide the entire protocol analysis process. The consistency of analyzing subject’s protocol is checked by three operators. This paper reports our preliminary study of protocol analysis on design activities. In-depth report of this research will be given in our future work.
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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 | 0.208 | 0.248 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.011 | 0.007 |
| Science and technology studies | 0.006 | 0.017 |
| Scholarly communication | 0.015 | 0.021 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.005 | 0.014 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".