摘要
学习行为分析旨在揭示个体与群体的学习行为特征,并进一步探测学习行为与学习效果之间的关系。结合高职学生学习实际,基于对超星学习通平台上在线学习行为数据的采集,使用Excel和SPSS工具,对在线学习行为进行聚类分析,聚类结果将学习者分为学习懒惰型、积极应付型、认真拘谨型、认真活跃型,并建立在线学习行为与学习效果之间的回归模型。研究发现,任务点完成次数、学习次数、作业完成次数、平时测试结果是影响学习成绩的主要因素,最后提出精准教育管理、在线资源优化、教学方法创新等提高教育教学质量和在线学习效果的相关建议和措施。
Learning behavior analysis aims to reveal the learning behavior characteristics of individuals and groups,and can further probe the relationship between learning behavior and learning effects.With the actual learning situation of higher vocational students,based on the collection of learning behavior data on the Chaoxing Learning Platform,using Excel and SPSS tools,a cluster analysis of online learning behavior was carried out.The clustering result classifies learners into learning lazy learners,active coping types,serious and restrained types,and serious and active types,and establishes a regression model between online learning behavior and learning effect.The study finds that the number of task points completed,of studies,of homework assignments completed,and the usual test results are the main factors affecting learning performance.It finally puts forward some suggestions and measures to improve the quality of education and the effect of online learning,such as precise education management,optimization of online resources,and innovation of teaching methods.
作者
程光胜
CHENG Guangsheng(Department of Information and Intelligent Engineering,Ningxia Vocational College of Finance and Economics,Yinchuan 750021,China)
出处
《安徽开放大学学报》
2022年第4期30-36,47,共8页
Journal of Anhui Open University
基金
宁夏哲学社会科学(教育学)规划项目“人工智能环境下精准学习者模型及系统构建研究”(项目编号:20NXJC07)。
关键词
高职学生
在线学习行为分析
学习效果
聚类分析
回归分析
higher vocational students
online learning behavior analysis
learning effect
clustering analysis
regression analysis