Did the Student Engage in Academic Dishonesty on their Exam? Yes, No, and Shades of Grey in Decision Making
Bibliographic record
Abstract
In academia, there are guidelines as to what constitutes academic dishonesty, and how to report it. This leads to the assumption that when instances arise, there are clear yes or no answers to the questions: (a) did the student engage in academic dishonesty, and (b) how should the student be disciplined? Previous research has been conducted examining the behaviours students engage in and the repercussions, but less research has examined the cognitions and actions of the people who discover the instances of academic dishonesty. Therefore, the purpose of this study was to examine how participants make sense of potential academic dishonesty scenarios and the resulting actions they would take. We presented 201 preservice teachers with three scenarios: (a) sneaking answers into an exam, (b) having someone tell you the answers and (c) peeking at someone else’s answers. For each scenario, they had to respond to the items (1) to what extent do you consider the student’s behaviour as academic dishonesty, (2) What in the story helped you decide on your response? and (3) What do you think is an appropriate form of discipline? Overall, participants strongly agreed the behaviours were academically dishonest, however, when asked to indicate what in the story helped them decide, the majority made embellishments to the story, and close to half of the participants provided their opinions related to academic dishonesty more broadly. Moreover, participants indicated a wide range of disciplines for the same scenarios. The results will be utilized to create discussion around decision-making and academic dishonesty.
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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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.015 |
| 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; both teacher heads agree on what is shown here.
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".