摘要
数据的全方位、多模态采集与分析成为了教育领域的重要研究导向,离群点检测技术能够从海量动态教育数据中定位异常数据,识别不符合一般特征规律的行为路径,为学习者的个性化指导和教育决策提供有价值的信息。文章通过梳理离群点检测技术在教育中应用的相关文献,发现离群点在教育中主要对学习者、教师、教育资源、学校、地区等作用对象进行离群分析,并在此基础上构建了教育教学中离群点检测技术的应用框架和应用流程。最后,文章就离群点检测技术应用于教育教学时面临的挑战进行了探讨,以期为离群点检测技术在教育领域的深入应用提供借鉴。
The comprehensive and multi-modal data acquisition and analysis of data has become an important research direction in the field of Education. Outlier detection technology can locate abnormal data from massive dynamic educational data, identify behavior paths that do not conform to the general characteristics, and provides valuable information for students＇ personalized instruction and educational decision-making. By reviewing relevant literature on the application of outlier detection technology in education, this study found that the main outlier objects in education are students, teachers, educational resources, schools and regions. Based on that, this article constructed the application framework and application process of outlier detection technology in education and teaching. Finally, the study discussed the challenges of outlier detection technology in education, expecting to provide reference and guidance for the in-depth application of outlier detection technology in education.
作者
陈世超
杨现民
潘青青
邢蓓蓓
CHEN Shi-chao;YANG Xian-min;PAN Qing-qing;XING Bei-bei(Research Center of Smart Education,Jiangsu Normal University,Xuzhou,Jiangsu,China 221116)
出处
《现代教育技术》
CSSCI
北大核心
2018年第6期101-107,共7页
Modern Educational Technology
基金
江苏省333工程科研基金资助项目"网络环境下深度学习行为分析及其促进策略研究"(项目号:333GC201702)
江苏高校"青蓝工程"资助
作者简介
陈世超，在读硕士，研究方向为技术增强学习与教育大数据，邮箱为chenshichao323@163．com。
