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

作　　者：赵立 郑怡 赵均榜 张芮 方方[5] 傅根跃 李康[6] ZHAO Li;ZHENG Yi;ZHAO Junbang;ZHANG Rui;FANG Fang;FU Genyue;KANG Lee(Department of Psychology,Hangzhou Normal University,Hangzhou 311121,China;Jing Hengyi School of Education,Hangzhou Normal University,Hangzhou 311121,China;College of Child Development and Education,Zhejiang Normal University,Hangzhou 311231,China;Hangzhou Xiayan Elementary School,Hangzhou 31112,China;School of Psychological and Cognitive Sciences and Beijing Key Laboratory of Behavior and Mental Health,Peking University,Beijing 100871,China;Ontario Institute for Studies in Education,University of Toronto,Ontario M5R 2X2,Canada)

机构地区：[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

出　　处：《心理学报》2024年第2期239-254,共16页Acta Psychologica Sinica

基　　金：国家自然科学基金项目(32171060);浙江省教育厅一般科研项目(Y202250508)资助。

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。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 scho

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 

分 类 号：B849[哲学宗教—应用心理学] G44[哲学宗教—心理学]