A theory that explains how and what learning occurs when utilizing the Classic case method
Bibliographic record
Abstract
The academic literature is replete with descriptions of how the Harvard (Classic) Case Method is taught. However, the available empirical research on the merits of the case method is flawed in a number of ways including an unclear definition of the case method, failure to have constructs that explain how learning occurs, and inadequate measures of learning. As a result, in spite of its broad usage, there is no empirical research that unambiguously shows that the Classic Case Method (CCM) leads to learning. In order to effectively conduct this research it is important to have an underlying theoretical framework to guide the testing of hypotheses but, surprisingly, the literature provides no such theory. Our paper fills this important gap by providing, for the first time, a theory of how and what learning occurs when employing the CCM. Our theory provides several contributions to the literature including; a clear definition of the CCM, specific constructs that explain how learning occurs, and integration and synthesis of several well established theories from the adult learning literature and how they may be applied to the CCM. In addition, the paper shows how the theory may be used by teachers, in a practical way, to guide their course design activities and achieve their teaching goals. • A theory of how and what learning occurs through use of the Classic Case Method. • Six key constructs in developing the theory. • Implications for further research. • Implications for teachers and students.
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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 | 0.026 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.004 | 0.036 |
| Scholarly communication | 0.011 | 0.018 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".