Subjects: Other Disciplines >> Synthetic discipline submitted time 2023-10-09 Cooperative journals: 《心理学报》
Abstract： A new generation of interactive models, called digital voice assistants (DVAs), can respond to young children's speech requests automatically and interact with them by voice. Research on the development of young children's epistemic trust in DVAs is scarce. Previous research has concentrated on the development and influencing factors of young children's epistemic trust in human informants or traditional electronic media (e.g., computers, webpages, internet). The semisocial nature of these devices determines the specific theoretical and practical value of investigating young children's epistemic trust in DVAs. Based on this, the purpose of the current study was to investigate the epistemic trust of young children (aged 3-6) and adults in DVAs in various domains and to confirm the significance of accuracy in their trust.The paradigm of dual-informant sources was employed in both experiments. A sample size of 88 children was required for an effect size of w = 0.30, 1 - β = 0.8, α = 0.05, according to G*Power 3.1. In Experiment 1, 30 adults and 90 children aged 4-6 were given testimony from distinct information sources (DVAs vs. humans) in either the natural or social domain to investigate the children's willingness to ask questions, explicit trust judgments, and final endorsements. Whereas the natural domain involved a task to label novel things, the social domain involved inquiry into social customs. The accuracy of the informants was manipulated in Experiment 2, which was based on Experiment 1, and 90 children aged 3-5 and 30 adults were exposed to various informants.The research participants were asked questions about their willingness to ask, explicit trust judgments, and final endorsements. The results of Experiment 1 showed that the children preferred to ask the DVAs questions about the natural domain rather than the social domain, with the DVAs being preferred overall. Moreover, the 6-year-old children preferred the DVAs as the information source more than the 4- to 5-year-old children. The adults were more likely to trust the DVAs than the young children. The results of Experiment 2 revealed that the children of all ages and adults were more likely to accept correct informant testimony in both the natural and social domains. In other words, the children were more likely to use the current accuracy of informants as a cue to assess and decide which informant to trust, and when the DVAs lost their accuracy, the children's preference disappeared along with their intellectual trust. The preference for accurate informants was more obvious in the adults and 4- to 5-year-olds than in the 3-year-olds, with the 3-year-olds being less sensitive to accuracy. Accuracy was an essential indicator of the DVAs' dependability.Our study is the first to investigate the development of epistemic trust in DVAs among children aged 3-6 in China. The results show that children can use DVAs as a source of information and knowledge. Young children become more likely to believe the testimonies of DVAs as they grow older. Children are more likely to trust DVAs in the natural domain than in the social domain. Furthermore, young children are more likely to accept the testimony of reliable informants. The results of this study may contribute to our understanding of the usability and utility of human interaction with technological systems and offer suggestions for the use of DVAs in homes and classrooms to support early learning.
Subjects: Psychology >> Social Psychology submitted time 2023-03-28 Cooperative journals: 《心理科学进展》
Abstract： In the digital era, young children have become increasingly exposed to electronic picture books, both at home and in educational settings. Most electronic picture books are programmed to be interactive such as being functional with dictionary interactivity. Dictionary interactivity provides young children an explanation of the word and clarifies its meaning with the animation. The effect of dictionary interactivity on picture book reading among young children has been the focus of research for the past two decades. To summarize the contribution of dictionary interactivity on the young children’s reading engagement, vocabulary learning and story comprehension, we conducted a systematic review of the literature.This review identified that the dictionary interactivity facilitates young children’s engagement in reading electronic picture books. With the support of dictionary interactivity, young children achieve similar results in reading electronic picture books as they engaged in shared reading with adults with hardcopy picture books. Compared to traditional pictures books, the advantage of electronic books with dictionary interactivity in promoting children’ engagement is not obvious. We propose that this result is partially due to the assessments of children’s reading engagement in previous studies that lack objectiveness.In terms of vocabulary learning, the review found the dictionary interactivity offer the similar educational affordance as print picture book on vocabulary learning. Young children reading electronic picture book with dictionary interactivity learn more words that appear in the book, and young children progressed the most after reading the electronic picture book with dynamic dictionary and the printed focal words. The effectiveness of dictionary interactivity in the support of vocabulary learning among children with special needs is well recognized, yet its effect on children from different socioeconomic status remains debatable. Inconsistent findings exist in different empirical studies regarding the effect of dictionary interactivity on children’s story comprehension. It is difficult to draw a conclusion due to the research design and the assessment of story comprehension that varied in previous studies. The factors including the content of text, the means of interactions with dictionary interactivity during reading, and children’s individual differences were identified that may impact the effect of dictionary interactivity on children’s reading comprehension. Findings are discussed in relation to the cognitive load theory and multi-media learning theory.This review proposed a few revenues for future studies. Firstly, to assess children’s reading engagement more effectively, the application of eye-trackers and bio-feedback instrument in future research may help to achieve more solid evidence. Secondly, in addition to explore the content of text and types of vocabulary that have impact on children’s vocabulary learning with dictionary interactivity, the factors relating to children’s initiative and the complexity of dictionary interactivity merit further exploration. Thirdly, intervention study is necessary to promote young children’s reading engagement, vocabulary learning and story comprehension, based on their individual characteristics. Finally, most existing research focuses on one aspect of children reading experiences (reading engagement, vocabulary acquisition, reading comprehension) that are associated with the function of dictionary interactivity, yet little attention focuses on the mechanisms underpinning the factors that impact the three different aspects in general. Future research that explores the underlying mechanisms form maximizing the function of dictionary interactivity in promoting reading experiences with electronic picture books is needed.
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.
submitted time 2023-03-25 Cooperative journals: 《心理科学进展》
Abstract： In the digital era, young children have become increasingly exposed to electronic picture books, both at home and in educational settings. Most electronic picture books are programmed to be interactive such as being functional with dictionary interactivity. Dictionary interactivity provides young children an explanation of the word and clarifies its meaning with the animation. The effect of dictionary interactivity on picture book reading among young children has been the focus of research for the past two decades. To summarize the contribution of dictionary interactivity on the young children’s reading engagement, vocabulary learning and story comprehension, we conducted a systematic review of the literature.This review identified that the dictionary interactivity facilitates young children’s engagement in reading electronic picture books. With the support of dictionary interactivity, young children achieve similar results in reading electronic picture books as they engaged in shared reading with adults with hardcopy picture books. Compared to traditional pictures books, the advantage of electronic books with dictionary interactivity in promoting children’ engagement is not obvious. We propose that this result is partially due to the assessments of children’s reading engagement in previous studies that lack objectiveness.In terms of vocabulary learning, the review found the dictionary interactivity offer the similar educational affordance as print picture book on vocabulary learning. Young children reading electronic picture book with dictionary interactivity learn more words that appear in the book, and young children progressed the most after reading the electronic picture book with dynamic dictionary and the printed focal words. The effectiveness of dictionary interactivity in the support of vocabulary learning among children with special needs is well recognized, yet its effect on children from different socioeconomic status remains debatable. Inconsistent findings exist in different empirical studies regarding the effect of dictionary interactivity on children’s story comprehension. It is difficult to draw a conclusion due to the research design and the assessment of story comprehension that varied in previous studies. The factors including the content of text, the means of interactions with dictionary interactivity during reading, and children’s individual differences were identified that may impact the effect of dictionary interactivity on children’s reading comprehension. Findings are discussed in relation to the cognitive load theory and multi-media learning theory.This review proposed a few revenues for future studies. Firstly, to assess children’s reading engagement more effectively, the application of eye-trackers and bio-feedback instrument in future research may help to achieve more solid evidence. Secondly, in addition to explore the content of text and types of vocabulary that have impact on children’s vocabulary learning with dictionary interactivity, the factors relating to children’s initiative and the complexity of dictionary interactivity merit further exploration. Thirdly, intervention study is necessary to promote young children’s reading engagement, vocabulary learning and story comprehension, based on their individual characteristics. Finally, most existing research focuses on one aspect of children reading experiences (reading engagement, vocabulary acquisition, reading comprehension) that are associated with the function of dictionary interactivity, yet little attention focuses on the mechanisms underpinning the factors that impact the three different aspects in general. Future research that explores the underlying mechanisms form maximizing the function of dictionary interactivity in promoting reading experiences with electronic picture books is needed.
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.
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