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首页> 外文会议>International Conference on Advanced Education, Management and Social Science >Behavioral Patterns of Completers in Massive Open Online Courses (MOOCs): The Use of Learning Analytics to Reveal Student Categories
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Behavioral Patterns of Completers in Massive Open Online Courses (MOOCs): The Use of Learning Analytics to Reveal Student Categories

机译：大规模开放在线课程（MOOCS）的完成者的行为模式：使用学习分析来揭示学生类别

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The learning energy of MOOC completers has important reference value for future learners. Existing research focuses on the behavioral performance of dropouts and participants, but ignores the mining of completer behavior patterns. In this paper, Kmeans clustering method, descriptive statistics, one-way analysis of variance, and chi-square test were used to systematically study the behavioral characteristics and academic performance of 1,388 MOOC completers. The results show that there are significant differences in resource and task preferences, effort levels, and so on, and learners can be divided into hard-working harvesters and punch-in participants; there is no significant difference in demographic characteristics. The article proposes strategies for improving teaching and promoting ordinary learners to improve learner behavior patterns and learning efficiency.
机译：MooC Termenter的学习能源对未来学习者具有重要的参考价值。 现有研究侧重于辍学和参与者的行为绩效，但忽略了随意的行为模式模式。 在本文中，kmeans聚类方法，描述性统计数据，方差单向分析和奇方检验用于系统地研究1,388 MooC完整者的行为特征和学术表现。 结果表明，资源和任务偏好，努力级别等显着差异，学习者可以分为勤奋的收割机和拳击参与者; 人口特征没有显着差异。 文章提出了改进教学和促进普通学习者的策略，以提高学习者行为模式和学习效率。

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