Academic cheating in early childhood: Role of age, gender, personality, and self-efficacy
Bibliographic record
Abstract
The current study investigated the association of children's age, gender, ethnicity, Big Five personality traits, and self-efficacy with their academic cheating behaviors. Academic cheating is a rampant problem that has been documented in adolescents and adults for nearly a century, but our understanding of the early development and factors influencing academic cheating is still weak. Using Zoom, the current study recruited children aged 4 to 12 years (N = 388), measured their cheating behaviors through six tasks simulating academic testing scenarios, and assessed their Big Five personality traits and self-efficacy through a modified Berkeley Puppet Interview paradigm, as well as age and gender. We found that children cheated significantly less with increased age and that boys cheated significantly more than girls. However, neither Big Five personality traits nor self-efficacy were significantly correlated with children's cheating. These findings suggest that academic cheating is a developing issue from early to middle childhood and that factors such as gender socialization may play a role in such development. Personal characteristics such as personality traits and self-efficacy may undergo additional development before their associations with cheating become robust, as reported in the adult literature.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".