An Educator's Framework for Modern Computer-Aided Design Instruction
Bibliographic record
Abstract
Computer-Aided Design (CAD) is a central tool for communication and design in engineering industries. While CAD tools are commonly found in engineering curricula across Canada, relatively few resources exist regarding best practices and considerations for teaching CAD. To contribute to these resources, we sought to co-create a comprehensive list of learning outcomes and teaching considerations for CAD instruction. We did so by undertaking an iterative reflective practice to capture our lived experience as CAD educators and researchers using a Visual CAD Learning Framework. We present this framework along with a discussion of trade-offs along the dimensions of learning outcomes, activities, and assessments. We hope that educators use this framework to reflect on their own practices of CAD instruction, fostering better informed choices about course content and instruction techniques so that students are better prepared to become effective engineering designers.
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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 | 0.024 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.014 | 0.005 |
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".