xMOOC中的互动:完成课程的学习者的视角

IF 3.2 Q1 EDUCATION & EDUCATIONAL RESEARCH
Hengtao Tang
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引用次数: 0

摘要

摘要低保留率成为大规模在线开放课程(MOOCs)的规模效益权衡。为了解决这种权衡,了解学习者成功完成mooc课程的经验是必要的。完成MOOC课程并达到课程通过要求的“热心完成者”倾向于积极参与课程互动;然而,他们关于互动如何促进课程完成的声音却无人知晓。因此,本研究采用定性方法,探讨了MOOC中学习者的互动体验,以及他们对各种互动类型(如学习者-内容、学习者-学习者、学习者-讲师、学习者-界面、学习者-自我和学习者-外部互动)的看法。这项研究的发现为现有的关于互动的证据增加了敏锐的完成者的声音,重点是每种类型的互动如何帮助完成MOOC。讨论了在mooc中保持学习者有效互动体验的实际意义。关键词:慕课烧结、敏锐完成者筛选、定性研究披露声明作者未报告潜在利益冲突。这项工作得到了南卡罗来纳大学教务长办公室的支持[80004720];南卡罗来纳大学研究副校长办公室[80003684]。汤胜涛,南卡罗莱纳大学学习设计与技术系副教授。他的研究兴趣集中在自我调节学习、多模态数据分析和人工智能(AI)在教育中的交叉。具体而言,恒涛应用多模态数据分析来了解学习者如何在技术增强的学习环境中调节自己的学习和协作解决问题,从而创建人工智能驱动的支架,以促进学习者对STEM职业的性格、知识、技能和行动结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Interactions in an xMOOC: perspectives of learners who completed the course
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.
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来源期刊
Open Learning
Open Learning EDUCATION & EDUCATIONAL RESEARCH-
CiteScore
10.00
自引率
12.50%
发文量
22
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