摘要
现代化线上教育技术在教育教学中的应用受到国家和各大高校的高度重视.为了将线上教学结果有机结合到高校教育过程,将通过线上学习平台提取学生的学习过程数据,对学生个人的学习行为及学习效果进行分析预测,旨在打破传统课堂教学过程中的“填鸭式”“满堂灌”的陋习.将收集的粗粒度数据进一步细粒度化,得到细粒度参数,从而发现学生的隐性数据,如学习兴趣、学习态度等.实验结果表明,细粒度指标在预测学生的总评成绩上起到了至关重要的作用.通过粗粒度指标及细粒度指标,归类出五种不同类型的学生群体,确定若干典型行为特征,为教师对学生学习行为干预给予针对性地参考.
The application of modern online educational technology in education and teaching has been highly valued by the state and universities.In order to combine the online education outcome with the process of university education,this study is targeted to pick up learning behavior index of undergraduates to effectively predict individual learning behavior and learning efficiency,through online platform.The purpose is to break the bad habits of cramming and filling the classroom in the process of traditional classroom teaching.Further refine the collected coarse-grained data to obtain fine-grained parameters and discover students′hidden data,such as learning interest,learning attitude and so on.The experimental results show that fine-grained indicators play an important role in predicting students′overall grades.Through the analysis of indicators,identify five different types of student groups and several typical behavior characteristics are determined,so as to have a deeper understanding of students′learning progress.
作者
刘金凤
徐展
兰朝凤
LIU Jinfeng;XU Zhan;LAN Chaofeng(School of Measurement Control Technology and Communication Engineering,Harbin University of Science and Technology,Harbin 150080,China)
出处
《高师理科学刊》
2023年第12期93-98,共6页
Journal of Science of Teachers＇College and University
基金
黑龙江省高等教育教学改革项目(SJGY20210384)。
关键词
细粒度参数
学习行为分类
数据提取
特征重要性判别
fine-grained parameters
learning behavior classification
data extraction
discrimination of feature importance
