Identifying Computer-Aided Design Action Types From Professional User Analytics Data
Bibliographic record
Abstract
Abstract Inspired by popular personality type indicators, we develop a framework for classifying individuals by their computer-aided design (CAD) behaviours. We are motivated by the trend of modern CAD software towards cloud platforms and expanded collaborative features. Cloud-CAD platforms enable collaboration by increasing access, and reducing conflicts and barriers to file-sharing. In order to generate insight to support CAD collaboration, we analyze the real-world data from an industry partner’s product development project, consisting of eight professional designers working on a cloud-CAD platform. This data corresponds to more than 1,420,000 actions over a span of eight months. Via hierarchical clustering, we group 79 unique CAD activities into 14 categories of CAD action groups, such as Part Studio, Assembly, Comment, View/Scan and Export. Next, we identify the degree to which each of the eight designers performs activity in these CAD action groups. We demonstrate the usefulness of this framework by highlighting insights revealed by the CAD action group mapping, confirmed via discussion with the industry partner. This CAD-type behaviour framework provides a tool for assessment and reflection on the types of behavioural tendencies present or missing on a team of designers. It can assist CAD educators and trainees in understanding their comprehensive CAD learning trajectory. Future extensions of the framework could leverage artificial intelligence techniques to provide real-time feedback on designer roles.
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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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".