Characterizing Computer Aided Design Performance: What can we learn from expert users?
Bibliographic record
Abstract
Students who participate in engineering technical extracurricular teams such as Mini Baja and Formula Electric tend to have excellent Computer Aided Design (CAD) skills that often exceed that of their curricular peers with similar levels of education. This study characterized the differences in CAD performance between student CAD “experts” and their more novice peers. It was initially hypothesized that the baseline spatial ability between groups will be similar, but that specific differences in the quality of the technical drawings would exist between the groups. Three groups of students were studied: “Senior” students (Year 3 or more and on an extracurricular technical team, n=12), “Novice” (Year 2, not on a team, n=13) and “Junior” (Year 2 and on a team, n=7). Participants completed the same CAD task and a spatial test. The task output, technical drawings, were rated against a rubric by an expert assessor blinded to group membership. The scores were analyzed in MATLAB with a Mann-Whitney U-test. Junior and Senior students scored higher on a spatial visualization test compared to Novices, but only the Novice-Junior comparison met the criteria for statistical significance. There were no significant differences on any technical drawing criteria between Juniors and Seniors. Seniors’ assembly drawings were significantly better than Novices on the criteria relating to parts list and labelling. This contributed to a statistically significant difference in the overall assembly drawing score between the Novice and Senior groups, even though there was no significant differences in total overall score (part design, part drawing, assembly drawing summed together) between any groups or any significant correlations with spatial ability (measured by PSVT-R). Differences relating to expertise level (group) were primarily related to the more complicated assembly drawing task.
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How this classification was reachedexpand
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".