Relating Case Presentation Style and Level of Student Knowledge to Fact Acquisition and Application in Accounting Case Analyses
Bibliographic record
Abstract
We examine how the presentation of accounting cases in narrative (i.e., story-based) or expository (i.e., fact-based) style combines with the level of student knowledge to affect two key dimensions of accounting case analyses: acquisition and application of relevant case facts. Results from an experiment with 210 undergraduate students in an introductory financial accounting class indicated that level of student knowledge (measured by course grade) was associated with the acquisition of accounting case facts through a statistically significant main effect. That is, high-knowledge students acquired a greater number of relevant facts from an accounting case than did low-knowledge students. The main effect of case presentation style and its interactive effect with student knowledge on case-fact acquisition were not statistically significant. In a subsequent problem-solving task, the interaction between case presentation style and student knowledge level exerted a statistically significant effect on the application of accounting case facts. Specifically, low-knowledge students were better able to apply case facts when the case was presented in narrative rather than expository style, whereas high-knowledge students performed equally well regardless of case presentation style. Implications of these results for instructors' case-selection decisions are discussed, and directions for future research are outlined.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.127 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".