Characterizing Computer Aided Design Mastery: What differences exist between expert and novice users?
Bibliographic record
Abstract
The goal of this work is to characterize “expert” student CAD use and examine whether differences from novices are related to factors such as gender, seniority, co-op experience, and spatial ability. Participants were recruited from a second-year mechanical engineering course and extra-curricular engineering technical teams. The groups were Novice (Year 2, not on a team, n=13), Junior (Year 2, on a team, n=7), Senior (Year 3+, on a team, n=12). Participants completed a spatial task (PSVT:R, Purdue Spatial Visualization Test) and a CAD task while being asked to describe their process (verbal protocol analysis). Groups were similar in gender composition. The spatial task (PSVT:R) performance was not related to gender, year of study, or use of CAD on co-op. Spatial performance was related to team participation. Two major differences in process were coded. One was references to past work. Seniors were more likely to reference co-op experience than Novices (p=0.03), Juniors were more likely to reference academic work compared to Seniors (p=0.04). The other significant difference was the method used for construction. Seniors were less likely to use a “cut away” approach to making their part than Novices (p=0.004) or Juniors (p=0.002). Seniors were more likely to trace geometry and extrude their part compared to Novices (p=0.004). The difference in referenced prior work presents an opportunity for instructors as many more students participate in co-op than in extra-curricular teams. This could be leveraged by encouraging students to contextualize learning from co-op or by creating industrially-relevant assignments. The difference in approach to creating the object suggests that CAD instructors could be introducing students to higher level tools earlier. This could be done through assignments that authentically demand more complicated geometries.
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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 | 0.000 |
| 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".