Effective Use of Case Teaching in Large Undergraduate Classes
Bibliographic record
Abstract
Aim/Purpose: To guide faculty who wish to use the case method in large undergraduate classes Background: The paper reviews a range of case teaching methods and provides specific guidance on how to use them in various classroom situations. Methodology: Literature review, reflective experience, interviews, and surveys Contribution: This paper addresses a gap in case teaching research which tends to focus on its use in graduate classes Findings: Case teaching can be used effectively in large undergraduate classes, but needs to be used in different ways and with different techniques from those commonly recommended for graduate classes. Recommendations for Practitioners: Be creative and go beyond the Harvard: case method and draw on the broader range of techniques used in active and experiential learning Impact on Society: Better and more relevant classroom experiences Future Research: Examine and evaluate field examples of innovative case teaching, especially in hybrid and online 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 | 0.037 | 0.098 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".