Innovations in the case method of teaching management, 2000–2024: A scoping review
Bibliographic record
Abstract
Beginning in the Harvard Business School over 100 years ago, the case method of training management students is now widely used throughout the world. While retaining its core philosophy the original version of the case method has been modified repeatedly. This has been necessitated due to the challenges of implementing the method in a variety of contexts; especially the changing nature of the student body, and advances in technology. The purpose of this paper is to review in one place the more recent innovations in the case method of teaching in business schools and the motivation for these innovations as found in the academic literature since the dawn of the 21st century. We also speculate on some of the teaching innovations we can anticipate in future. • Since the turn of the century there have been many innovations in the classic case method. • Innovations stimulated by internationalization, changing student body and technology change. • Paper identifies five broad classes of innovations depending on the problem they are solving. • It also speculates on possible future case method teaching innovations.
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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 | 0.028 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.027 | 0.031 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| 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".