Strategies for Effective Case Method Teaching in Large Undergraduate Business Classes
Bibliographic record
Abstract
This study finds that the case method pedagogy, originally designed for use with graduate students, can also be effective in large, undergraduate business classes. While most of the case method research has historically focused on the graduate level, the method has now become popular for undergraduate teaching. There is a growing need for large class pedagogy and best practices due to the fast growing enrolment in North American business schools, and this study presents a preliminary opinion on how the case method could be adapted to meet the needs of large and diverse classes. The study reviews the challenges of teaching cases, and the challenges of using active learning techniques in large class teaching. The study presents a definition for “large” and “very large” classes in the context of case teaching, and suggests several practices from case-teaching instructors that can be used to teach the method effectively in these environments.
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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 | 0.032 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.005 |
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".