{"title":"在线学习环境中情绪调节在预测由元情绪介导的情绪投入中的作用:一个两阶段SEM-ANN方法","authors":"Changqin Huang, Linjie Zhang, Tao He, Xuemei Wu, Yafeng Pan, Zhongmei Han, Wenzhu Zhao","doi":"10.1080/01443410.2023.2254524","DOIUrl":null,"url":null,"abstract":"Abstract Understanding the mechanism of emotion regulation and the formation of emotional engagement can improve online learning persistence and academic performance. This study was set to pinpoint the potential pathways between emotion regulation and emotional engagement through meta-emotion and develop a predictive model for online emotional engagement. The data collected from 302 college students were analysed using a two-stage structural equation modelling-artificial neural network approach. Firstly, the path analysis implied the significant linkages from emotion regulation to emotional engagement through emotional repair. Secondly, the artificial neural network analysis results suggested that emotional repair contributed to the development of emotional engagement most, and the current model predicted emotional engagement with an accuracy of 91.1%. The main contribution of the present study is providing empirical evidence to predict emotional engagement from novel perspectives through a two-stage approach.","PeriodicalId":48053,"journal":{"name":"Educational Psychology","volume":"43 1","pages":"736 - 755"},"PeriodicalIF":3.6000,"publicationDate":"2023-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"The role of emotion regulation in predicting emotional engagement mediated by meta-emotion in online learning environments: a two-stage SEM-ANN approach\",\"authors\":\"Changqin Huang, Linjie Zhang, Tao He, Xuemei Wu, Yafeng Pan, Zhongmei Han, Wenzhu Zhao\",\"doi\":\"10.1080/01443410.2023.2254524\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Abstract Understanding the mechanism of emotion regulation and the formation of emotional engagement can improve online learning persistence and academic performance. This study was set to pinpoint the potential pathways between emotion regulation and emotional engagement through meta-emotion and develop a predictive model for online emotional engagement. The data collected from 302 college students were analysed using a two-stage structural equation modelling-artificial neural network approach. Firstly, the path analysis implied the significant linkages from emotion regulation to emotional engagement through emotional repair. Secondly, the artificial neural network analysis results suggested that emotional repair contributed to the development of emotional engagement most, and the current model predicted emotional engagement with an accuracy of 91.1%. The main contribution of the present study is providing empirical evidence to predict emotional engagement from novel perspectives through a two-stage approach.\",\"PeriodicalId\":48053,\"journal\":{\"name\":\"Educational Psychology\",\"volume\":\"43 1\",\"pages\":\"736 - 755\"},\"PeriodicalIF\":3.6000,\"publicationDate\":\"2023-08-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Educational Psychology\",\"FirstCategoryId\":\"102\",\"ListUrlMain\":\"https://doi.org/10.1080/01443410.2023.2254524\",\"RegionNum\":2,\"RegionCategory\":\"心理学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"EDUCATION & EDUCATIONAL RESEARCH\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Educational Psychology","FirstCategoryId":"102","ListUrlMain":"https://doi.org/10.1080/01443410.2023.2254524","RegionNum":2,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"EDUCATION & EDUCATIONAL RESEARCH","Score":null,"Total":0}
The role of emotion regulation in predicting emotional engagement mediated by meta-emotion in online learning environments: a two-stage SEM-ANN approach
Abstract Understanding the mechanism of emotion regulation and the formation of emotional engagement can improve online learning persistence and academic performance. This study was set to pinpoint the potential pathways between emotion regulation and emotional engagement through meta-emotion and develop a predictive model for online emotional engagement. The data collected from 302 college students were analysed using a two-stage structural equation modelling-artificial neural network approach. Firstly, the path analysis implied the significant linkages from emotion regulation to emotional engagement through emotional repair. Secondly, the artificial neural network analysis results suggested that emotional repair contributed to the development of emotional engagement most, and the current model predicted emotional engagement with an accuracy of 91.1%. The main contribution of the present study is providing empirical evidence to predict emotional engagement from novel perspectives through a two-stage approach.
期刊介绍:
This journal provides an international forum for the discussion and rapid dissemination of research findings in psychology relevant to education. The journal places particular emphasis on the publishing of papers reporting applied research based on experimental and behavioural studies. Reviews of relevant areas of literature also appear from time to time. The aim of the journal is to be a primary source for articles dealing with the psychological aspects of education ranging from pre-school to tertiary provision and the education of children with special needs. The prompt publication of high-quality articles is the journal"s first priority. All contributions are submitted "blind" to at least two independent referees before acceptance for publication.