基于网络学习行为聚类分析的在线课堂优化策略研究

doi:10.16039/j.cnki.cn22-1249.2020.08011

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摘要

利用聚类分析法对网络学习行为分类，从而挖掘不同类型学习者在学习行为和学习效果间的差异。以大学计算机公共课《数据库技术及应用》为例，综合学习者学习行为类别差异、在线课程内容组织以及课程特点等要素为在线课堂进一步发展提供优化策略。

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	白雪

关键词: 网络学习行为聚类分析在线课堂优化策略

Abstract:

Cluster analysis is used to classify network learning behaviors, so as to explore the differences between learning behaviors and learning effects of different types of learners. Taking Database Technology and Application as an example, this paper provides an optimization strategy for the further development of online classroom based on the differences of learners' learning behaviors, online course content organization and course characteristics.

Key words: network learning behavior cluster analysis online classroom optimization strategy

出版日期: 2020-08-25 发布日期: 2020-08-25 整期出版日期: 2020-08-25

ZTFLH:

G434

引用本文:

白雪. 基于网络学习行为聚类分析的在线课堂优化策略研究 [J]. 吉林化工学院学报, 2020, 37(8): 44-48.
BAI Xue. Research on Online Classroom Optimization Strategy Based on Cluster Analysis of Network Learning Behavior . Journal of Jilin Institute of Chemical Technology, 2020, 37(8): 44-48.

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http://xuebao.jlict.edu.cn/CN/10.16039/j.cnki.cn22-1249.2020.08011 或 http://xuebao.jlict.edu.cn/CN/Y2020/V37/I8/44

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