The Effectiveness of Alternative Training Techniques on Analytical Procedures Performance*
Bibliographic record
Abstract
This study examines the effectiveness of alternative training techniques on improving analytical procedures performance. To account for the sequential and iterative nature of complex analytical procedures, we used two different knowledge acquisition mechanisms (worked-out example and problem solving training) to develop specific training tools that have not been used in prior audit research. In addition, we combined these mechanisms with different levels of self-explanation (with self-explanation or without self-explanation). Participants were provided with no training (baseline group) or six training techniques that included outcome feedback, worked-out example, problem solving, self-explanation, worked-out example with self-explanation and problem solving with self-explanation. Thereafter, participants completed a final analytical procedures case that had an error seeded in the financial statements. Results indicate that "worked-out example" or "problem solving training" combined with self-explanation outperformed all other groups. Additional analyses provide insight on the performance gains of the various training techniques in the initial hypothesis generation and subsequent information search and evaluation phases during analytical procedures. Our findings contribute to the training literature in auditing and psychology by showing that combinations of techniques are required for designing effective training for complex diagnostic decision making like analytical procedures.
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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 | 0.008 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".