摘要
为了深入分析线上学习行为与学习成绩的关联性,利用工具SPSS20.0,对268名同学的SPOC和MOOC平台中的在线学习数据进行挖掘。首先通过单变量分析法分析四类在线学习行为与学习成绩的显著性,在此基础上,采用聚类分析对学习行为进行序列转换分析,探讨不同行为序列学习者的学习效果差异性,以优化学习者在线学习行为的有效路径。不仅帮助学习者转换学习路径,及时调整学习行为和反思错题原因,提高在线学习效率,也帮助教师和管理者了解学生的学习行为特点,对在线学习的个性化路径构建有借鉴意义。
In order to deeply analyze the correlation between online learning behaviors and academic performance, the online learning data in SPOC and MOOC platforms of 268 students are mined using the tool SPSS 20.0. The significance of four categories of online learning behaviors and academic performance is analyzed by univariate analysis. Based on this, a sequence conversion analysis of learning behaviors is conducted using cluster analysis to explore the difference of learning effects of learners with different behavioral sequences, so as to optimize the effective paths of learners’ online learning behaviors. It not only helps learners switch their learning paths, adjust their learning behaviors and reflect on the reasons for their mistakes in time to improve their online learning efficiency, but also helps teachers and administrators better understand the characteristics of students’ learning behaviors, which has implications for the construction of personalized paths for online learning.
作者
王文晶
闫俊伢
Wang Wenjing;Yan Junya(Shanxi Vocational University of Engineering Science and Technology,College of Information Engineering,Taiyuan,Shanxi 030619,China)
出处
《计算机时代》
2022年第9期115-118,共4页
Computer Era
基金
山西省教育科学“十三五规划课题”研究成果“HLW-20165基于学习行为序列的在线学习诊断与干预”(2020.11-2022.9)
2020年横向课题“在线学习支撑系统的构建研究”(HX2020029)。
关键词
学习成绩
聚类分析
行为序列
个性化路径
academic performance
cluster analysis
behavior sequences
personalized paths
