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基于K-means算法的在线学习行为聚类研究

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在线学习是近年来随着互联网的发展而逐渐兴起的一种学习方式,它的便捷性和丰富的学习资源吸引了越来越多的学习者。随着在线学习平台日益普及,海量的用户数据也随之产生。如何从这些数据中提取有价值的信息,促进教育教学质量提升是当前值得思考的重要课题。文章介绍了基于K均值聚类算法(K-means Clustering Algorithm,K-means)的在线学习行为聚类分析方法,为在线学习平台提供了重要的数据分析和应用支持,帮助教师及平台管理者及时调整教学模式和教学策略,以提升学习者的在线学习效果。
Research on clustering of online learning behaviors based on the K-means algorithm
Online learning is a learning method that has gradually emerged in recent years with the development of the Internet.Its convenience and rich learning resources have attracted more and more learners.With the increasing popularity of online learning platforms,massive user data is also generated.How to extract valuable information from these data and promote the improvement of education and teaching quality is an important topic worth thinking about at present.This paper introduces the clustering analysis method of online learning behaviors based on the K-means clustering algorithm algorithm,and empirically proves the effectiveness of this method,providing important data analysis and application support for online learning platforms,and helping teachers and platform managers to adopt teaching strategies and teaching modes in a timely manner to improve the effectiveness of user online learning.

online learningbehavioral clustering analysisK-means algorithmloyalty

韩树河、王颖、王海、李慧勇

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江苏航运职业技术学院 智能制造与信息学院,江苏 南通 226010

在线学习 行为聚类分析 K-means算法 忠诚度

江苏省高校哲学社会科学研究项目

2021SJA1659

2024

无线互联科技
江苏省科学技术情报研究所

无线互联科技

影响因子:0.263
ISSN:1672-6944
年,卷(期):2024.21(3)
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