REALIZING CEAB GRADUATE ATTRIBUTES USING THE CASE METHOD
Bibliographic record
Abstract
The University of Waterloo (UW) offers seven fully accredited and internationally competitive engineering undergraduate programs. It has the largest undergraduate co-operative education program in the world, and the largest Engineering program in Canada. All UW engineering departments are refining and evaluating their undergraduate curricula in order to address the new Canadian Engineering Accreditation Board (CEAB) requirements, with its focus on learning outcomes and graduate attributes. This new perspective provides an opportunity for alternative pedagogical approaches for developing and assessing graduate attributes. Over the past several years, the Waterloo Cases in Design Engineering (WCDE) group at UW has been developing and promoting the use of engineering design cases throughout the curriculum. These cases are developed primarily from our own students’ work term experience. Cases provide an effective pedagogical method to integrate students’ technical knowledge as well as develop appropriate engineering skills. Engineering cases help students understand and better appreciate the complexity of engineering practice, and gain valuable experience in engineering problem solving and working in teams. They naturally complement the real-world work experience they get on their co-op terms. The primary objective of this paper is to present the use of case studies to promote active learning and assessment of engineering design with a focus on the CEAB graduate attributes. Some case studies have been developed to focus on specific CEAB attributes. An example case study will be presented, its implementation discussed, and the effectiveness of achieving the targeted learning outcomes will be discussed.
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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 | 0.018 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".