{"title":"利用解释结构模型研究影响在线学习参与的因素","authors":"Wang Lin","doi":"10.1145/3516529.3516534","DOIUrl":null,"url":null,"abstract":"With the development of the Internet, online courses are becoming increasingly popular and traditional education methods are developing towards modern education supported by the Internet. Participation in online learning is the crucial factor that influences students’ learning efficiency and outcomes. For obtaining a structural model diagram of factors that influence participation in online learning, this paper, based on Interpretive Structural Modeling (ISM) for participation in online learning, creates an adjacency matrix to compute reachability matrix by analyzing the relation among 15 factors by means of literature and interview questionnaires, and then undertakes hierarchical decomposition of influencing factors. Finally, this paper proposes countermeasures and suggestions from three perspectives of students, teachers, platforms and resources.","PeriodicalId":205338,"journal":{"name":"2021 2nd Artificial Intelligence and Complex Systems Conference","volume":"22 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Factors that Influence Participation in Online Learning Using an Interpretive Structural Modeling\",\"authors\":\"Wang Lin\",\"doi\":\"10.1145/3516529.3516534\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"With the development of the Internet, online courses are becoming increasingly popular and traditional education methods are developing towards modern education supported by the Internet. Participation in online learning is the crucial factor that influences students’ learning efficiency and outcomes. For obtaining a structural model diagram of factors that influence participation in online learning, this paper, based on Interpretive Structural Modeling (ISM) for participation in online learning, creates an adjacency matrix to compute reachability matrix by analyzing the relation among 15 factors by means of literature and interview questionnaires, and then undertakes hierarchical decomposition of influencing factors. Finally, this paper proposes countermeasures and suggestions from three perspectives of students, teachers, platforms and resources.\",\"PeriodicalId\":205338,\"journal\":{\"name\":\"2021 2nd Artificial Intelligence and Complex Systems Conference\",\"volume\":\"22 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-10-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 2nd Artificial Intelligence and Complex Systems Conference\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3516529.3516534\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 2nd Artificial Intelligence and Complex Systems Conference","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3516529.3516534","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Factors that Influence Participation in Online Learning Using an Interpretive Structural Modeling
With the development of the Internet, online courses are becoming increasingly popular and traditional education methods are developing towards modern education supported by the Internet. Participation in online learning is the crucial factor that influences students’ learning efficiency and outcomes. For obtaining a structural model diagram of factors that influence participation in online learning, this paper, based on Interpretive Structural Modeling (ISM) for participation in online learning, creates an adjacency matrix to compute reachability matrix by analyzing the relation among 15 factors by means of literature and interview questionnaires, and then undertakes hierarchical decomposition of influencing factors. Finally, this paper proposes countermeasures and suggestions from three perspectives of students, teachers, platforms and resources.