结合学习分析的mooc文献综述

Zhonggen Yu
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引用次数: 5

摘要

尽管有很多研究致力于将mooc与学习分析相结合,但很少有研究系统地回顾了相关文献。本研究采用聚类技术、书目网络可视化、内容分析和STARLITE方法,从学习分析模型、mooc平台、学习分析效果、参与效果、自我调节效果、动机效果、在线互动效果以及mooc与学习分析结合效果的其他影响因素等方面系统综述了相关文献。为mooc的设计者、研究者、学习者和讲师提供建设性的建议,以降低学习者的辍学率,提高完成率,增强学习者的参与度,获得更好的学习效果。未来对mooc的研究可以关注学习分析的改进,因为学习分析对mooc的有效性有至关重要的影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Literature Review on MOOCs Integrated With Learning Analytics
Although there have been numerous studies committed to MOOCs integrated with learning analytics, fewer of them have systematically reviewed the related literature. Using clustering techniques, bibliographic network visualization, content analysis, and STARLITE method, this study systematically reviews the literature in terms of learning analytics models, platforms of MOOCs, effect of learning analytics, effect of engagement, self-regulation, motivation, and online interactions, as well as other influencing factors of the effectiveness of MOOCs integrated with learning analytics. It provides constructive suggestions for designers, researchers, learners, and instructors of MOOCs so as to lower down learner dropout rates, increase completion rates, enhance learner engagement, and better learning outcomes. Future research into MOOCs could focus on the improvements on learning analytics because learning analytics could exert an essential influence on the effectiveness of MOOCs.
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