摘要
文章基于新冠疫情前后四川某高校在线学习平台的学生学习数据变化,获取疫情前后学生在在线平台学习的学习数据,针对疫情前后的变化数据进行两个方面的主题分析:数据分析和数据挖掘。其中数据分析采用了均值过程和单因素分析方法,数据挖掘构建了决策树模型和贝叶斯聚类模型。分析和挖掘结果能够直观得出疫情变化对该高校学生的学习造成的影响,能够为高校提供远程学习的管理意见,提高学生的学习效率。
Based on the change of students’learning data in the online learning platform of a college in Sichuan before and after the COVID-19 epidemic,this paper gets the students’learning data in online platform before and after the epidemic.According to the changed data before and after the epidemic,two aspects of subject analysis are carried out:data analysis and data mining.The data analysis adopts the mean process and single factor analysis method,and the data mining constructs the decision tree model and Bayesian clustering model.The analysis and mining results can intuitively obtain the impact of epidemic changes on the learning of college students,can provide management opinions on distance learning for colleges,and improve students’learning efficiency.
作者
王钰杰
杨杉
WANG Yujie;YANG Shan(School of Computer and Software,Jincheng College of Sichuan University,Chengdu 611731,China)
出处
《现代信息科技》
2021年第14期94-97,102,共5页
Modern Information Technology
