CAD Challenges App: An Informatics Framework for Parametric Modeling Practice and Research Data Collection in Computer-Aided Design
Bibliographic record
Abstract
Abstract Computer-aided design (CAD) is a key tool in modern engineering design and manufacturing, making design education and design research with CAD important fields of study. Effectively teaching modelling strategies in traditional classrooms is challenging and the design research community faces barriers in participant recruitment for research studies. In this paper, we propose a framework that connects the teaching and research community with design informatics in CAD. We productized the framework, named the “CAD Challenges” web application, and integrated it to Onshape, a commercially available cloud-CAD software. With free and easy access to this app, users gain access to a library of modelling challenges from within an Onshape document. The app automatically evaluates submissions and provides feedback, enabling asynchronous learning and the development of CAD expertise through practice. After challenge attempts, data on both the design process and the completed model are collected, enabling insight into the different strategies that can be used to create the same geometry. While providing a free and accessible training tool for learners, the big data generated through challenge attempts can provide valuable insight into how students learn CAD and the modeling strategies used by experts. Benefits and opportunities enabled by the framework are discussed in detail with preliminary research analysis.
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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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.021 | 0.012 |
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".