Cheating among elementary school children: A machine learning approach

Abstract

Academic cheating is common, but little is known about its early emergence. It was examined among Chinese second to sixth graders (N = 2094; 53% boys, collected between 2018 and 2019) using a machine learning approach. Overall, 25.74% reported having cheated, which was predicted by the best machine learning algorithm (Random Forest) at a mean accuracy of 81.43%. Cheating was most strongly predicted by children's beliefs about the acceptability of cheating and the observed prevalence and frequency of peer cheating at school. These findings provide important insights about the early development of academic cheating, and how to promote academic integrity and limit cheating before it becomes entrenched. The present research demonstrates that machine learning can be effectively used to analyze developmental data.

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