FIRST YEAR ENGINEERING DESIGN – GUELPH’S TEDDY BEAR WHEEL CHAIR EXPERIENCE
Bibliographic record
Abstract
First year engineering design courses arenow common across Canadian engineering schools.These courses can be challenging to develop and deliver.They are often stuck in the chicken versus egg problem.Can I teach design with no engineering? Can I teachengineering with no design? How does one introducefour years of engineering education and an engineeringcareer in one course? How to do so across many or allengineering disciplines? How to do so in a foundationalmanner? Can it be done in a meaningful way? Can it beengaging and fun? A Teddy Bear Wheel Chair (TBWC)design project is the focal point of Guelph’s first yearengineering design course. The TBWC integratescomputers, mechanics, biomechanics (Teddy Bear style),environment, safety, sustainability, materials, costing,hands-on, perseverance, ethics and DESIGN. The TBWCparticipates in curling, sprinting and scoring goals. Theresult is a challenging and fun competition thatintroduces all of Guelph’s engineering students to theirengineering design careers. This paper and presentationwill share one instructor’s efforts to make all of this work.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.045 | 0.009 |
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".