Department of Psychology, Hangzhou Normal University, Hangzhou, China.
Jing Hengyi School of Education, Hangzhou Normal University, Hangzhou, China.
Child Dev. 2023 Jul-Aug;94(4):922-940. doi: 10.1111/cdev.13910. Epub 2023 Feb 8.
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.
学术作弊很常见，但对其早期出现的情况却知之甚少。本研究使用机器学习方法对中国二至六年级学生（N=2094；男生占 53%，于 2018 年至 2019 年间收集）进行了调查。总体而言，有 25.74%的学生报告曾作弊过，最佳机器学习算法（随机森林）的平均准确率为 81.43%，能够预测出学生的作弊行为。儿童对作弊的可接受性的信念，以及在学校观察到的同伴作弊的普遍性和频率，对作弊行为的预测作用最强。这些发现为学术作弊的早期发展以及如何在其根深蒂固之前促进学术诚信和限制作弊提供了重要的见解。本研究表明，机器学习可以有效地用于分析发展数据。
