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Cheating among elementary school children: A machine learning approach.

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  • 1Department of Psychology, Hangzhou Normal University, Hangzhou, China.

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Academic cheating emerges early in elementary school, with over 25% of students reporting it. Machine learning identified beliefs about cheating acceptability and peer behavior as key predictors, offering insights for promoting academic integrity.

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Area of Science:

  • Developmental Psychology
  • Educational Psychology
  • Machine Learning Applications

Background:

  • Academic cheating is prevalent but its early developmental origins remain understudied.
  • Understanding the onset of cheating is crucial for developing effective interventions.
  • Previous research has not extensively utilized advanced analytical techniques to explore this phenomenon in young learners.

Purpose of the Study:

  • To investigate the early emergence and predictors of academic cheating in elementary school students.
  • To apply machine learning techniques to analyze developmental data on cheating behavior.
  • To identify key factors influencing cheating to inform strategies for promoting academic integrity.

Main Methods:

  • A machine learning approach, specifically Random Forest, was employed to analyze data from 2094 Chinese second to sixth graders.
  • Data collection occurred between 2018 and 2019.
  • Predictive modeling was used to identify significant factors associated with self-reported cheating.

Main Results:

  • 25.74% of students reported engaging in academic cheating.
  • The Random Forest algorithm accurately predicted cheating behavior with 81.43% mean accuracy.
  • Children's beliefs regarding the acceptability of cheating and the observed prevalence and frequency of peer cheating were the strongest predictors.

Conclusions:

  • Academic cheating behavior can emerge and be predicted in elementary school-aged children.
  • Machine learning is an effective tool for analyzing developmental data in educational research.
  • Findings highlight the importance of addressing students' perceptions of cheating and peer influences to foster academic integrity from an early age.