Using environmental nudges to reduce academic cheating in young children
Bibliographic record
Abstract
Previous research on nudges conducted with adults suggests that the accessibility of behavioral options can influence people's decisions. The present study examined whether accessibility can be used to reduce academic cheating among young children. We gave children a challenging math test in the presence of an answer key they were instructed not to peek at, and manipulated the accessibility of the answer key by placing various familiar objects on top of it. In Study 1, we used an opaque sheet of paper as a two-dimensional occluder, and found that it significantly reduced cheating compared to a transparent plastic sheet. In Study 2, we used a three-dimensional occluder in the form of a tissue box to make the answer key appear even less accessible, and found it was significantly more effective in reducing cheating than the opaque paper. In Study 3, we used two symbolic representations of the tissue box: a realistic color photo and a line drawing. Both representations were effective in reducing cheating, but the realistic photo was more effective than the drawing. These findings demonstrate that manipulating accessibility can be an effective strategy to nudge children away from cheating in an academic context. They further suggest that different types of everyday objects and their symbolic representations can differentially impact children's moral behavior.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".