LISTERIOSIS CASE STUDY – VARIATION AND LONGEVITY FOR CURRICULUM ENHANCEMENT
Bibliographic record
Abstract
Both teaching and learning from case studies enriches the engineering curriculum by connecting the classroom to real world complexities. A case study about the 2008 Listeriosis outbreak at the Maple Leaf Foods facility in Toronto was developed for the Food Process Engineering course, ChE 564. ChE 564 is a fourth-year technical elective in Chemical Engineering at the University of Waterloo (Waterloo), offered once every winter term. The Listeriosis case was developed by Waterloo Cases in Design Engineering (WCDE) from publicly-available sources.The WCDE Listeriosis case study has been used in four offerings of ChE 564 by three instructors, from 2013 to 2016. Factors that influenced the successful transfer of teaching material are explored using instructor reflection, classroom observations, and student feedback. The three instructors reflected on these factors between each offering of the course and adapted their teaching methodologies to align with the learning outcomes for the course.The evolution of the WCDE Listeriosis case study and its longevity will be discussed over the four course offerings. Issues such as student expectations, the role of the instructor, the open-ended nature of the case, class size, and class engagement are discussed as well. The success and challenges of the Listeriosis case study have broader implications on the difficulties of transferring material between terms and instructors while balancing variation for different cohorts. One challenge when developing case study material is balancing the time invested with the rewards in the classroom and the uptake by different instructors and/or courses.
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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 | 0.019 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| 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".