{"title":"Interactions in an xMOOC: perspectives of learners who completed the course","authors":"Hengtao Tang","doi":"10.1080/02680513.2023.2259922","DOIUrl":null,"url":null,"abstract":"ABSTRACTThe low retention rate becomes a scale-efficiency tradeoff for Massive Open Online Courses (MOOCs). To resolve this tradeoff, understanding learner experience of successfully completing MOOCs is necessary. Keen completers , who complete a MOOC and meet the requirement of passing the course, tend to actively participate in course interactions; however, their voices about how interactions contribute to course completion are unheard. Therefore, this study applied a qualitative methodology to explore keen completers’ interaction experience and their perceptions of various types of interaction (e.g. learner-content, learner-learner, learner-instructor, learner-interface, learner-self, and learner-exterior interaction) in a MOOC. The findings of this study have added keen completers’ voices to existing evidence about interactions with a focus on how each type of interaction aided in the completion of a MOOC. Practical implications about maintaining learners’ effective interaction experience in MOOCs are discussed.KEYWORDS: MOOCsinteractionkeen completersretentionqualitative study Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThe work was supported by the Office of the Provost, University of South Carolina [80004720]; Office of the Vice President for Research, University of South Carolina [80003684].Notes on contributorsHengtao TangHengtao Tang is an associate professor of Learning Design and Technologies at the University of South Carolina. His research interests address the intersection of self-regulated learning, multimodal data analytics, and artificial intelligence (AI) in education. Specifically, Hengtao applies multimodal data analytics to understand how learners regulate their learning and their collaborative problem solving in technology-enhanced learning environments and thereby creating AI-driven scaffolds to facilitate learners' disposition, knowledge, skills, and action outcomes toward STEM careers.","PeriodicalId":46089,"journal":{"name":"Open Learning","volume":null,"pages":null},"PeriodicalIF":3.2000,"publicationDate":"2023-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Open Learning","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1080/02680513.2023.2259922","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"EDUCATION & EDUCATIONAL RESEARCH","Score":null,"Total":0}
引用次数: 0
Abstract
ABSTRACTThe low retention rate becomes a scale-efficiency tradeoff for Massive Open Online Courses (MOOCs). To resolve this tradeoff, understanding learner experience of successfully completing MOOCs is necessary. Keen completers , who complete a MOOC and meet the requirement of passing the course, tend to actively participate in course interactions; however, their voices about how interactions contribute to course completion are unheard. Therefore, this study applied a qualitative methodology to explore keen completers’ interaction experience and their perceptions of various types of interaction (e.g. learner-content, learner-learner, learner-instructor, learner-interface, learner-self, and learner-exterior interaction) in a MOOC. The findings of this study have added keen completers’ voices to existing evidence about interactions with a focus on how each type of interaction aided in the completion of a MOOC. Practical implications about maintaining learners’ effective interaction experience in MOOCs are discussed.KEYWORDS: MOOCsinteractionkeen completersretentionqualitative study Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThe work was supported by the Office of the Provost, University of South Carolina [80004720]; Office of the Vice President for Research, University of South Carolina [80003684].Notes on contributorsHengtao TangHengtao Tang is an associate professor of Learning Design and Technologies at the University of South Carolina. His research interests address the intersection of self-regulated learning, multimodal data analytics, and artificial intelligence (AI) in education. Specifically, Hengtao applies multimodal data analytics to understand how learners regulate their learning and their collaborative problem solving in technology-enhanced learning environments and thereby creating AI-driven scaffolds to facilitate learners' disposition, knowledge, skills, and action outcomes toward STEM careers.