Subjects: Psychology >> Social Psychology submitted time 2023-03-27 Cooperative journals: 《心理学报》
Abstract： Background. 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. Method. 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. Results. 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. Conclusion. 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.
Subjects: Physics >> General Physics: Statistical and Quantum Mechanics, Quantum Information, etc. submitted time 2023-03-16 Cooperative journals: 《心理学报》
Abstract： Background. 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. Method. 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. Results. 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. Conclusion. 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.
Subjects: Psychology >> Educational Psychology Subjects: Psychology >> Developmental Psychology submitted time 2022-12-01
Abstract：
Background. 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.
Method. 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.
Results. 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.
Conclusion. 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.
Peer Review Status:
Awaiting Review
Subjects: Biology >> Zoology submitted time 2018-12-24 Cooperative journals: 《动物营养学报》
Abstract：本试验旨在研究妊娠后期饲粮代谢能水平对母羊增重、血浆生殖激素浓度、初乳产量、初乳乳成分及羔羊初乳期生长的影响。选用18只体重（39.75±2.86） kg、年龄3～4岁，处于妊娠91 d的内蒙古白绒山羊，随机分为3组，每组6只。根据NRC制订基础饲粮，各组饲粮代谢能分别为7.70（基础饲粮的70%）、11.00（基础饲粮，对照组）、14.30 MJ/kg（基础饲粮的130%）。预试期为妊娠91~100 d，正试期为妊娠101 d至产后第5天。结果表明：1）14.30 MJ/kg饲粮代谢能可显著提高妊娠后期血浆雌二醇（E2）浓度平均值、妊娠后期母羊体增重、产后第1~4天初乳乳蛋白含量及初乳期羔羊平均日增重（P＜0.05）；母羊饲粮代谢能水平对母羊血浆孕酮（P4）及催乳素（PRL）浓度、初乳乳脂含量（产后第4天除外）、羔羊初生重影响不显著（P＞0.05）。2）7.70 MJ/kg饲粮代谢能可降低妊娠后期母羊血浆E2、P4及PRL浓度平均值，妊娠后期母羊体增重，羔羊初生重，初乳产量（产后第5天除外），初乳乳蛋白含量（产后第1、3天除外），初乳期羔羊平均日增重，但影响不显著（P＞0.05），显著降低初乳乳脂含量（产后第3天除外）。因此，饲粮代谢能为14.30 MJ/kg更适合妊娠后期母羊，为应对饲草料不足可以以代谢能为7.70 MJ/kg的饲粮限饲。
Subjects: Biology >> Bioengineering Subjects: Physics >> Electromagnetism, Optics, Acoustics, Heat Transfer, Classical Mechanics, and Fluid Dynamics submitted time 2017-02-13
Abstract：[目的] 评价经颅磁电抑郁症（失眠）治疗仪（商品名：奥博百忧度）治疗抑郁症的有效性及安全性。[方法] 对 80例轻、中度抑郁症患者进行了随机、安慰对照、多中心4周临床试验,其中治疗组和对照组各 40例。治疗组使用经颅磁电抑郁症（失眠）治疗仪进行治疗，对照组使用经颅磁电抑郁症（失眠）治疗仪模拟治疗（音频安慰）。[结果] 临床试验研究结果表明，治疗 4 周后对照组的总显效率和总有效率分别为5.00%（2/40）和 35.00%（14/40），其 95%的可信区间分别为（0.00～11.75）和（20.22～49.78），治疗组的总显效率和总有效率分别为 65.00%（26/40）和 80.00%（32/40），其 95%的可信区间分别为（50.22～79.78）和（67.60～92.40），各中心效应有差别（P=0.0009）；两组总有效率和总显效率的优效性检验 P<0.0001，且治疗组高于对照组，说明治疗组优于对照组。两组均无不良反应。[结论] 经颅磁电抑郁症（失眠）治疗仪（商品名：奥博百忧度）治疗抑郁症使用安全，疗效确切，尤其在“抑郁、有罪感、睡眠障碍、工作和兴趣、迟缓、激惹、焦虑”等主要症状方面得到明显改善。
Peer Review Status:Awaiting Review
Subjects: Biology >> Bioengineering Subjects: Biology >> Neurobiology Subjects: Physics >> Electromagnetism, Optics, Acoustics, Heat Transfer, Classical Mechanics, and Fluid Dynamics submitted time 2017-02-13
Abstract：[目的] 评价经颅磁电脑病治疗仪（商品名：奥博阿尔茨海默治疗仪）治疗轻、中度血管性痴呆(Vascular dementia，VD)的有效性及安全性。 [方法] 对80例轻、中度AD[Hachinski缺血量表评分≥7分，痴呆程度（CDR=1.0）或（CDR=2.0）]患者进行了随机、安慰对照、多中心4周临床试验,其中治疗组和对照组各40例。所有入选病例均给予正规的内科基础治疗和规范化护理，治疗组使用经颅磁电脑病治疗仪进行治疗，对照组使用模拟经颅磁电脑病治疗仪进行模拟治疗。 [结果] 治疗4周时,治疗组较对照组简易精神状态评价（MMSE）、临床痴呆程度量表( CDR) 和日常生活能力评价（ADL）分数显著改善（组间差异P依次<0.0001、0.05、0.05）。两组均无不良反应。 [结论] 经颅磁电脑病治疗仪治疗轻、中度血管性痴呆具有治疗效果，对患者的精神状态、认知行为和日常生活自理能力有较好的改善作用，且该治疗仪使用安全。
Peer Review Status:Awaiting Review
Subjects: Biology >> Bioengineering Subjects: Physics >> Electromagnetism, Optics, Acoustics, Heat Transfer, Classical Mechanics, and Fluid Dynamics submitted time 2017-02-03
Abstract：[目的] 评价经颅磁电脑病治疗仪（商品名：奥博阿尔茨海默治疗仪）治疗轻、中度阿尔茨海默病(Alzheimer disease，AD)的有效性及安全性。[方法] 对 80 例轻、中度 AD[Hachinski 缺血量表评分≤4 分，痴呆程度（CDR=1.0）或（CDR=2.0）]患者进行了随机、安慰对照、多中心8周临床试验,其中治疗组和对照组各40例。所有入选病例均给予正规的内科基础治疗和规范化护理，治疗组使用经颅磁电脑病治疗仪进行治疗，对照组使用安慰经颅磁电脑病治疗仪进行模拟治疗。[结果] 临床试验研究结果表明，治疗8周时,治疗组较对照组简易精神状态评价（MMSE）、阿尔茨海默认知评价（ADAS-Cog）和日常生活能力评价（ADL）分数显著改善（组间差异P依次<0.001、0.0001、0.05）。治疗 4周时，MMSE和 ADAS分数已有提高(组间差异 P依次<0.05、0.01)。两组均无不良反应。[结论] 根据试验统计结果，经颅磁电脑病治疗仪治疗轻、中度阿尔茨海默病具有治疗效果，对患者的精神状态、认知行为和日常生活自理能力有较好的改善作用，且该治疗仪使用安全。
Peer Review Status:Awaiting Review
Hits
Hits
Downloads
Comment