Should Case Materials Precede or Follow Lectures?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper examines a fundamental question faced by all accounting educators who use accounting case analyses alongside lecture-based instruction: Does it matter whether cases precede or follow a lecture? Results from a controlled experiment indicate that students' case analysis performance is initially enhanced when a lecture precedes a case, because the lecture equips students with knowledge to apply to the case and it constrains the number of irrelevant ideas that students apply to the case. However, there is a drawback to positioning a lecture before the first case—it constrains the number of relevant ideas that students generate themselves to apply to the case. In addition, the short-term benefits of positioning a lecture before case analyses disappear when students analyze a second case. Specifically, we find that performance on a second case is significantly better when students have previously learned in an environment that places the first case before the lecture. Our evidence suggests that these case-before-lecture students are more likely to engage their pre-lecture knowledge and the knowledge obtained from the lecture when analyzing the second case. Practical implications for using cases with lectures are discussed.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it