Engineering Design Case Studies: Effective And Sustainable Development Methods
Bibliographic record
Abstract
Case studies and the case method of teaching and learning have demonstrated pedagogical benefits.Sustaining the effective and efficient development of cases requires strategies and methods that are proven and systematic.Waterloo Cases in Design Engineering (WCDE) is a unique program to enhance design engineering education by converting student co-op work term reports into case studies and implementing them across all courses in the Faculty of Engineering curriculum.Cases have been implemented successfully, and show promise in addressing and demonstrating new Canadian Engineering Accreditation Board (CEAB) graduate attribute requirements.The case method also shows promise in integrating these required attributes by expressing real situations encountered in practice and allowing individual students and student teams to experience realistic challenges in a classroom setting.In addition to developing cases from work term reports, cases have been developed from student capstone project experiences, Master of Engineering (MEng) design project experiences, and directly from the experiences of our industry partners.The development strategies and methods used to ensure effective and timely development of cases varies depending on the source used.This paper describes the development methods used to successfully develop sustainable sources of engineering design case studies, and offers lessons-learned perspectives from our development and implementation experiences.
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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 | 0.046 | 0.046 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".