Academic Fraud and Remote Evaluation of Accounting Students: An Application of the Fraud Triangle
Bibliographic record
Abstract
Abstract The pandemic has altered accounting education with the widespread adoption of remote evaluation platforms. We apply the lens of the fraud triangle to consider how the adoption of remote evaluation influences accounting students’ ethical values by measuring the incidence of cheating behavior as well as capturing their perceptions of their opportunity to cheat and their rationalization of cheating behavior. Consistent with prior research, our results show that cheating is higher in the online environment compared to remote evaluation, although the use of proctoring software in online evaluation appears to mitigate but not eliminate students’ the unethical behavior. However, cheating was not reduced when students attest to an honor code during the beginning of an exam. Nonetheless, we find that the use of both proctoring software and honor codes reduces students’ perceptions of opportunity and rationalization of cheating behavior. It follows that the remote evaluation environment may unintentionally be negatively influencing the ethicality of students and future accounting professionals by promoting cheating behavior and, by so doing, negatively influencing the development of unethical values of accounting students and future accounting professionals. Educators should consider the use of appropriate educational interventions to reduce the incidence and opportunities for unethical behavior and, by so doing, help promote the development of ethical values in future accounting professionals. Further implications for teaching and the accounting profession are discussed.
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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 | 0.026 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| 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".