Teaching with Cases in Disaster and Emergency Management Programs: Instructional Design Guidance
Bibliographic record
Abstract
There is a long history of the use of cases in teaching in post-secondary programs and some fields (e.g., law, medicine, business) have their own distinctive approach to the use of case-based learning methods. Within disaster and emergency management (DEM), which is a relatively new field of post-secondary study, there are as of yet no formally recognized approaches to the use of cases in teaching, and further there is limited research on the disciplinary characteristics of teaching practices in the DEM field. This article presents findings from a study that explored how and why cases are used in post-secondary DEM programs. The methodological approach to the study supported the development of a domain-based outcome theory that explains three different approaches for using case-based learning methods in DEM programs and the functions of cases relative to each of the different types of learning outcomes. This novel conceptual framework for teaching with cases was found to address deficiencies in existing schema for conceptualizing the use of cases in teaching.
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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 | 0.023 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 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".