TEN YEARS OF ENHANCING ENGINEERING EDUCATION WITH CASE STUDIES: INSIGHTS, LESSONS AND RESULTS FROM A DESIGN CHAIR
Bibliographic record
Abstract
Improving student learning and increasing connections between theory and engineering practice captures the main goals of Waterloo Cases in Design Engineering (WCDE). WCDE is a group at the University of Waterloo (Waterloo) that was established in 2005 as a part of the NSERC Chairs in Design Engineering program. WCDE works with instructors, industry and students to bring real life complexities to the classroom by using authentic case studies. Over the last ten years, more than 175 case studies have been developed and implemented in more than 100 courses with over 125 instructors, across all engineering disciplines. WCDE has collaborated with more than 140 industry, government, non-profit, and academic case partners for the development and implementation of case material.Surveys are used to gauge students’ receptivity to case implementations and for continuous improvement. Student feedback from WCDE case implementations are presented and discussed. The benefits and challenges of case study teaching are discussed, along with reflections on the next steps towards extensive use of engineering cases in education.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.027 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".