Explaining Differences in Learning Outcomes in Auditing Education - The Importance of Background Factors, Prior Knowledge and Intellectual Skills
Bibliographic record
Abstract
In this paper we use a learner perspective on learning outcomes which reflects that some students taking accounting classes are also provided with on-the-job training in accounting firms. Hence knowledge about learning outcomes for different groups of students is essential information for educators as well as the accounting profession. This paper extends prior research on the role of declarative and procedural knowledge in performing auditing tasks. Measuring learning outcomes is a complex matter requiring sensible measures for both declarative knowledge (ability to verbalize pertinent facts or processes) and procedural knowledge (intellectual skills). The performance of 75 graduate accounting students representing both types of schema is examined. The findings suggest that differences in learning outcomes may be attributed to differences in student background and prior knowledge (auditing experience). The findings also suggest that the importance of prior knowledge is mitigated by the intellectual skills required for a particular task.
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How this classification was reachedexpand
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".