Identifying Computer-Aided Design Action Types From Professional User Analytics Data
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it