Protocol Analysis in Engineering Design Education Research: Observations, Limitations, and Opportunities
Bibliographic record
Abstract
<strong>Background:</strong> One of the most popular methods for studying the cognitive processes of design and problem-solving activity is Protocol Analysis (PA). As such, PA has been widely used in engineering design education research. <strong>Purpose:</strong> The aim of this work is to describe how PA has been used in engineering design education contexts, understanding the range of research questions that can be addressed by the method as well as providing some commentary on the strengths, limitations, and future directions of the method. <strong>Scope/Method:</strong> We conduct a systematic review of the literature following the PRISMA method. A search combining key terms – protocol analysis, design, engineering, student – and their variants in the Scopus database resulted in 126 articles, which were further reduced to 45 through two rounds of abstract and full-text screening. The main inclusion criteria was that the work use PA as the method to investigate design activities in an engineering educational setting. <strong>Conclusions:</strong> The use of PA has significantly contributed to understanding the cognition of students engaged in design activities and to improving engineering design education. Technological advances enable new efficiencies in protocol collection and analysis, offering promising new directions in the use of PA in more authentic learning environments.
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 | 0.828 | 0.889 |
| Meta-epidemiology (narrow) | 0.002 | 0.004 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.011 | 0.018 |
| Science and technology studies | 0.007 | 0.020 |
| Scholarly communication | 0.013 | 0.020 |
| Open science | 0.006 | 0.011 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".