TEACHING CREDIBLE VALIDATION AND VERIFICATION METHODS TO A LARGE, MULTIDISCIPLINARY FIRST-YEAR ENGINEERING DESIGN CLASS
Bibliographic record
Abstract
Abstract – This paper describes our experiences in teaching credible validation and verification methods to a class of 250 first-year Engineering Science students at the University of Toronto. While our students have previously developed proof-of-concept prototypes, this was the first year that testing their prototypes against key design requirements – and substantially integrating stakeholder feedback into their projects – were course expectations. 
 Core strategies to support our students included leveraging the expertise of a multidisciplinary teaching team; training students to collect and interpret data from community stakeholders; demystifying prototyping and testing through small-scale activities; and legitimizing our expectations through real-world examples.
 Student design teams generally performed well with respect to validation and verification criteria on their summative project evaluations. Most teams effectively integrated stakeholder feedback with other research into developing and refining their designs, and demonstrated that their prototypes addressed key metrics. Challenges to be addressed in future course iterations are discussed.
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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 | 0.002 | 0.002 |
| 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 | 0.000 | 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".