Bibliographic record
Abstract
Waterloo Cases in Design Engineering (WCDE) was established with the support of the Natural Sciences and Engineering Research Council (NSERC), General Motors of Canada Limited (GMCL) and the University of Waterloo (UW) to enhance the teaching and learning of engineering design in all courses across the Faculty of Engineering using design case studies. The design case studies are developed from a large sustainable source of co-op engineering student work term reports. Communicating the benefits of case studies and the case method is best performed in an active learning format and environment. The workshop format is therefore an appropriate format to demonstrate the benefits of the case method for facilitating the learning of engineering design in particular, and professional practice knowledge and skills in general. The primary goal of this workshop is to educate participants in the use of case studies to promote active learning and assessment of engineering design and other ABET/CEAB graduate attribute requirements using the case method. A case study that was developed as an interrupted case study will be used to demonstrate learning and implementation of engineering design, where each module represents a phase in the design process. The workshop plan includes reflection and discussion after each module of the case is implemented.
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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.054 | 0.015 |
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".