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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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 | 0.019 | 0.160 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 0.009 |
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".