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Record W4392438336 · doi:10.1016/j.jecp.2024.105888

Academic cheating in early childhood: Role of age, gender, personality, and self-efficacy

2024· article· en· W4392438336 on OpenAlexafffund
Shawn Yee, Amy Xu, Kanza Batool, Tz-yu Duan, Catherine Ann Cameron, Kang Lee

Bibliographic record

VenueJournal of Experimental Child Psychology · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicAcademic integrity and plagiarism
Canadian institutionsOntario Institute for Cancer ResearchInstitute for Christian StudiesUniversity of British Columbia
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsCheatingPsychologyBig Five personality traitsPersonalityDevelopmental psychologySelf-efficacySocializationSocial psychology

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.861
Threshold uncertainty score0.950

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.032
GPT teacher head0.383
Teacher spread0.351 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2024
Admission routes2
Has abstractyes

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