人工智能方法在探究小学生作业作弊行为及其关键预测因子中的应用

doi:10.3724/SP.J.1041.2024.00239

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

人工智能方法在探究小学生作业作弊行为及其关键预测因子中的应用

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.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: zhaoli@hznu.edu.cn;李康, E-mail: kang.lee@utoronto.ca
作者简介:
#赵立和郑怡为共同第一作者
基金资助:
*国家自然科学基金项目(32171060);浙江省教育厅一般科研项目(Y202250508)

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. 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

摘要/Abstract

摘要：

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

Abstract:

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

中图分类号:

B849: G44

赵立, 郑怡, 赵均榜, 张芮, 方方, 傅根跃, 李康. (2024). 人工智能方法在探究小学生作业作弊行为及其关键预测因子中的应用. 心理学报, 56(2), 239-254.

ZHAO Li, ZHENG Yi, ZHAO Junbang, ZHANG Rui, FANG Fang, FU Genyue, KANG Lee. (2024). The application of artificial intelligence methods in examining elementary school students' academic cheating on homework and its key predictors. Acta Psychologica Sinica, 56(2), 239-254.

图/表 8

参考文献 59

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年级	年龄(岁)		性别		学校			合计
年级	均值	标准差	男	女	普通公办	民工子弟小学	民办小学	合计
2年级	7.86	0.60	205	192	210	83	104	397
3年级	8.79	0.40	209	189	200	84	114	398
4年级	9.79	0.33	209	176	186	85	114	385
5年级	10.74	0.33	240	211	183	131	137	451
6年级	11.75	0.34	253	214	155	173	139	467

年级	年龄(岁)		性别		学校			合计
年级	均值	标准差	男	女	普通公办	民工子弟小学	民办小学	合计
2年级	7.86	0.60	205	192	210	83	104	397
3年级	8.79	0.40	209	189	200	84	114	398
4年级	9.79	0.33	209	176	186	85	114	385
5年级	10.74	0.33	240	211	183	131	137	451
6年级	11.75	0.34	253	214	155	173	139	467

Item	Mean	SD
Q2. 后果严重性, 1~5
1. 受老师批评	3.05	1.22
2. 受老师惩罚	3.65	1.31
3. 受父母批评	3.24	1.25
4. 受父母惩罚	3.36	1.30
5. 受周围同学的批评或嘲笑	3.33	1.48
Q3. 自我可接受性	1.81	1.12
Q4. 同伴可接受性	2.13	1.17
Q5. 策略有效性, 1~9
1. 增加作弊后的惩罚力度	2.89	1.45
2. 老师批改作业更仔细	2.88	1.36
3. 加强课堂练习, 在课堂上把知识弄懂	3.28	1.50
4. 老师加强教育, 多强调作业作弊属于不良行为	2.47	1.39
5. 降低作业难度	2.12	1.39
6. 老师批评或惩罚	2.98	1.34
7. 给予独立完成作业的同学以表扬和奖励	2.88	1.42
8. 家长批评或惩罚	2.93	1.43
9. 做作业时父母监督	2.84	1.45
Q6. 同伴作弊−普遍性	2.02	0.88
Q7. 同伴作弊−总体频率	2.08	0.96
Q8. 同伴作弊−具体频率, 1~3
1. 抄他人的作业	2.01	1.05
2. 做作业时抄答案	1.91	1.04
3. 让他人帮写作业	1.24	0.61

Item	Mean	SD
Q2. 后果严重性, 1~5
1. 受老师批评	3.05	1.22
2. 受老师惩罚	3.65	1.31
3. 受父母批评	3.24	1.25
4. 受父母惩罚	3.36	1.30
5. 受周围同学的批评或嘲笑	3.33	1.48
Q3. 自我可接受性	1.81	1.12
Q4. 同伴可接受性	2.13	1.17
Q5. 策略有效性, 1~9
1. 增加作弊后的惩罚力度	2.89	1.45
2. 老师批改作业更仔细	2.88	1.36
3. 加强课堂练习, 在课堂上把知识弄懂	3.28	1.50
4. 老师加强教育, 多强调作业作弊属于不良行为	2.47	1.39
5. 降低作业难度	2.12	1.39
6. 老师批评或惩罚	2.98	1.34
7. 给予独立完成作业的同学以表扬和奖励	2.88	1.42
8. 家长批评或惩罚	2.93	1.43
9. 做作业时父母监督	2.84	1.45
Q6. 同伴作弊−普遍性	2.02	0.88
Q7. 同伴作弊−总体频率	2.08	0.96
Q8. 同伴作弊−具体频率, 1~3
1. 抄他人的作业	2.01	1.05
2. 做作业时抄答案	1.91	1.04
3. 让他人帮写作业	1.24	0.61

模型	均值(%)	标准差	95% 置信区间
模型	均值(%)	标准差	Lower	Upper
逻辑回归	77.87	1.50	77.72	78.01
XGBoost	77.82	1.69	77.63	78.02
MLP	78.25	1.70	78.01	78.48
随机森林	79.47	0.95	79.28	79.66
集成学习	80.46	0.80	80.30	80.62

人工智能方法在探究小学生作业作弊行为及其关键预测因子中的应用

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

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预测变量	均值	标准差	95% CI
预测变量	均值	标准差	Lower	Upper
民办小学与普通公办小学的对比	0.91	1.38	0.76	1.06
对“受周围同学的批评或嘲笑”这一作弊可能后果的严重性的评价	0.87	0.71	0.79	0.95
年级对比 (3年级 vs 2年级)	0.80	1.84	0.61	1.00
对“降低作业难度”这一减少作弊的策略的有效性评价	0.70	0.80	0.62	0.79
对“给予独立完成作业的同学以表扬和奖励”这一减少作弊的策略的有效性评价	0.68	0.60	0.61	0.74
对“做作业时父母监督”这一减少作弊的策略的有效性评价	0.63	0.59	0.56	0.69
对“老师加强教育, 多强调作业作弊属于不良行为”这一减少作弊的策略的有效性评价	0.59	0.60	0.53	0.66
对“老师批改作业更仔细”这一减少作弊的策略的有效性评价	0.58	0.52	0.52	0.64
对“受老师批评”这一作弊可能后果的严重性的评价	0.54	0.58	0.48	0.61
年级对比 (5年级 vs 2年级)	0.49	0.91	0.39	0.59
对“增加作弊后的惩罚力度”这一减少作弊的策略的有效性评价	0.47	0.52	0.42	0.53
对“老师批评或惩罚”这一减少作弊的策略的有效性评价	0.47	0.46	0.42	0.52
对“加强课堂练习, 在课堂上把知识弄懂”这一减少作弊的策略的有效性评价	0.46	0.49	0.41	0.52
对“家长批评或惩罚”这一减少作弊的策略的有效性评价	0.45	0.38	0.41	0.49
性别对比 (女生 vs 男生)	0.40	0.36	0.36	0.44
有弟弟/妹妹者与独生子女的对比	0.38	0.71	0.30	0.45
有哥哥/姐姐者与独生子女的对比	0.22	0.55	0.16	0.28
周围同学让他人帮写作业这一行为的频繁性	0.13	0.41	0.09	0.18
既有哥哥/姐姐又有弟弟/妹妹者与独生子女的对比	0.06	0.34	0.02	0.10

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