Behavioral Patterns of Completers in Massive Open Online Courses (MOOCs): The Use of Learning Analytics to Reveal Student Categories

Proceedings of the 2020 International Conference on Advanced Education, Management and Social Science (AEMSS2020) Pub Date : 2020-07-24 DOI:10.2991/assehr.k.200723.102

Changsheng Chen, Jingyun Long, Junxiao Liu, Zongjun Wang, Minglei Shan, Yuming Dou

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引用次数: 2

Abstract

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.

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大规模在线开放课程(MOOCs)完成者的行为模式:使用学习分析来揭示学生类别

MOOC完成者的学习能量对未来的学习者具有重要的参考价值。现有的研究侧重于辍学者和参与者的行为表现，而忽略了对完整行为模式的挖掘。本文采用Kmeans聚类法、描述性统计、单因素方差分析、卡方检验等方法，对1388名慕课完成者的行为特征和学业成绩进行了系统研究。结果表明:学习者在资源和任务偏好、努力程度等方面存在显著差异，可分为勤奋收获型和打孔型两类;人口统计学特征无显著差异。本文提出了改进教学和促进普通学习者的策略，以改善学习者的行为模式和学习效率。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Proceedings of the 2020 International Conference on Advanced Education, Management and Social Science (AEMSS2020)

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