ISSN 0439-755X
CN 11-1911/B

心理学报 ›› 2024, Vol. 56 ›› Issue (2): 239-254.doi: 10.3724/SP.J.1041.2024.00239

• “数智时代的道德伦理”特刊 • 上一篇    


赵立1,#(), 郑怡2,#, 赵均榜3, 张芮1,4, 方方5, 傅根跃1, 李康6()   

  1. 1.杭州师范大学心理学系
    2.杭州师范大学经亨颐教育学院, 杭州 311121
    3.浙江师范大学儿童发展与教育学院, 杭州 311231
    4.杭州市夏衍小学, 杭州 311121
    5.北京大学心理与认知科学学院, 行为与心理健康北京市重点实验室, 北京 100871
    6.加拿大多伦多大学, 安大略教育研究所, 安大略 M5R 2X2
  • 收稿日期:2022-07-14 发布日期:2023-12-01 出版日期:2024-02-25
  • 通讯作者: 赵立, E-mail:;李康, E-mail:
  • 作者简介:


  • 基金资助:

The application of artificial intelligence methods in examining elementary school students' academic cheating on homework and its key predictors

ZHAO Li1,#(), ZHENG Yi2,#, ZHAO Junbang3, ZHANG Rui1,4, FANG Fang5, FU Genyue1, KANG Lee6()   

  1. 1. Department of Psychology, Hangzhou Normal University, Hangzhou 311121, China
    2. Jing Hengyi School of Education, Hangzhou Normal University, Hangzhou 311121, China
    3. College of Child Development and Education, Zhejiang Normal University, Hangzhou 311231, China
    4. Hangzhou Xiayan Elementary School, Hangzhou 31112, China
    5. School of Psychological and Cognitive Sciences and Beijing Key Laboratory of Behavior and Mental Health, Peking University, Beijing 100871, China
    6. Ontario Institute for Studies in Education, University of Toronto, Ontario M5R 2X2, Canada
  • Received:2022-07-14 Online:2023-12-01 Published:2024-02-25


小学生作业作弊是心理学领域忽略已久的研究重点, 机器学习是数智时代新兴的人工智能科学。笔者对2, 098名2至6年级小学生进行问卷调查, 采用机器学习法, 考察个体认知、道德判断、同伴行为, 及性别、年级、成绩等因素对小学生作业作弊行为的影响。结果表明:集成机器学习模型对小学生作业作弊预测准确率(AUC均值)达80.46%; 对作业作弊预测效应最强的4个因素依次为个体对作业作弊的接受程度、观察到同伴作弊的普遍性和频率, 及其自身成绩。

关键词: 小学生, 诚信, 学业作弊, 作业作弊, 机器学习, 预测, 同伴行为


Academic cheating has been a challenging problem for educators for centuries. It is well established that students often cheat not only on exams but also on homework. Despites recent changes in educational policy and practice, homework remains one of the most important academic tasks for elementary school students in China. However, most of the existing studies on academic cheating for the last century have focused almost exclusively on college and secondary school students, with few on the crucial elementary school period when academic integrity begins to form and develop. Further, most research has focused on cheating on exams with little on homework cheating. The present research aimed to bridge this significant gap in the literature. We used the advanced artificial intelligence methods to investigate the development of homework cheating in elementary school children and the key contributing factors so as to provide scientific basis for the development of early intervention methods to promote academic integrity and reduce cheating.

We surveyed elementary school students from Grades 2 to 6 and obtained a valid sample of 2, 098. The questionnaire included students’ self-reported cheating on homework (the dependent variable). The predictor variables included children’s ratings of (1) their perceptions of the severity of consequences for being caught cheating, (2) the extent to which they found cheating to be acceptable, and the extent to which they thought their peers considered cheating to be acceptable, (3) their perceptions of the effectiveness of various strategies adults use to reduce cheating, (4) how frequently they observed their peers engaging in cheating, and (5) several demographic variables. We used ensemble machine learning (an emerging artificial intelligence methodology) to capture the complex relations between cheating on homework and various predictor variables and used the Shapley importance values to identify the most important factors contributing children’s decisions to cheat on homework.

Overall, 33% of elementary school students reported having cheated on homework, and the rate of such self-reported cheating behavior increased with grade. The best models with the ensemble machine learning accurately predicted the students’ homework cheating with a mean Area Under the Curve (AUC) value of 80.46%. The Shapley importance values showed that all predictors significantly contributed to the high performance of our computational models. However, their importance values varied significantly. Children’s cheating was most strongly predicted by their own beliefs about the acceptability of cheatings, how commonly and frequently they had observed their peers engaging in academic cheating, and their achievement level. Other predictors such as children’s beliefs about the severity of the possible consequences of cheating (e.g., being punished by one’s teacher), their beliefs about the effectiveness of cheating deterrence strategies (e.g., working harder) and demographic characteristics, though significantly, were not important predictors of elementary school children’s homework cheating.

This study for the first time examined elementary school students' homework cheating behavior. We used machine learning integration algorithms to systematically investigate the key factors contributing to elementary school students' homework cheating. The results showed that homework cheating already exists in the elementary school period and increases with grade. Advanced machine learning algorithms revealed that elementary school students' homework cheating largely depends on their acceptance of cheating, their peers' homework cheating, and their own academic performance level. The present findings advance our theoretical understanding of the early development of academic integrity and dishonesty and forms the scientific basis for developing early intervention programs to reduce academic cheating. In addition, this study also shows that machine learning, as the core method of artificial intelligence, is an effective method that can be used to analyze developmental data analysis.

Key words: elementary school students, honesty behavior, academic cheating, cheating on homework, machine learning, prediction, peer behavior