摘要
随着在线学习平台的普及,产生了大量学习行为数据,如何利用大数据挖掘技术分析在线学习行为,解决学习者经常面临的“资源过载”和“学习迷航”问题,更好地实现教学决策、学习过程优化和个性化学习方法推荐等,已经成为研究重点.文章基于苏州线上教育中心的学习行为数据,研究了常用的推荐系统模型,结合该平台的数据特点,提出了一种基于知识图谱的协同过滤推荐算法,利用该算法,平台推荐的资源准确率超过了90%,有效解决了学生“学习迷航”的问题.
With the popularity of online learning platform,a large number of learning behavior data are generated.How to use big data mining technology to analyze online learning behavior,to solve the problem that learners often face“resource overload”and“learning confusion”,better implementation of teaching decision-making,learning process optimization,personalized learning method recommendation,etc.,has become a research focus.Based on the learning behavior data of Suzhou online education center,this work studies the common recommendation system model.Combined with the data characteristics of the platform,a collaborative filtering recommendation algorithm based on knowledge map is proposed.With this algorithm,the accuracy of the platform’s recommended resources is more than 90%,which effectively solves the problem of“learning lost”for students.
作者
徐亚军
郭俭
XU Ya-Jun;GUO Jian(Suzhou Baizhitong Information Technology Co.Ltd.,Suzhou 215000,China)
出处
《计算机系统应用》
2020年第7期217-221,共5页
Computer Systems & Applications
关键词
在线教育
知识图谱
协同过滤
资源推荐
online education
knowledge map
collaborative filtering
resource recommendation
