DESIGN DAYS BOOT CAMP 2.0: IMPROVEMENTS AND CONNECTIONS TO CEAB GRADUATE ATTRIBUTES
Bibliographic record
Abstract
Engineering design is a critical skill that all engineering students are expected to learn and is often the focus of final year capstone projects and first-year cornerstone projects. In the Systems Design Engineering Department at the University of Waterloo, engineering design is introduced to the students during an intense two-day Design Days Boot Camp. Design Days was originally conceived of and run in Fall 2016. The Fall 2018 version, Design Days 2.0, included substantial improvements focused on adding two additional design activities and a writing activity, strengthening the connection with first year content, and providing a greater variety of team experiences. The methods of achieving the nine intended learning outcomes of Design Days 2.0 are discussed and connected to CEAB graduate attributes. This demonstrates that meaningful learning can be achieved during a two-day boot camp that will starts students on the path towards professional engineering. Other departments are encouraged to use the presented intended learning outcomes, graduate attributes connections, and Design Days 2.0 descriptions as a template for their own design boot camp. Finally, Design Days 2.0 inspired ideas for further improvements including the incorporation of a software-focused design activity, adding budgetary constraints, and providing an opportunity for student reflection.
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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 | 0.012 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.032 | 0.012 |
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".