面向学生画像的偏好知识获取研究
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摘要：
针对信息过载导致学生不能有效获取偏好知识的问题,提出一种面向学生画像的偏好知识获取方法.利用学生浏览知识内容,通过学生关键词、主题分布两个维度,构建学生画像向量空间模型.据此,计算学生与知识之间的相似度,获取直接偏好知识.利用学生浏览知识内容进行聚类分析,根据学生学习行为设计算法,获取间接偏好知识.以实际运行系统中提取的学生学习行为信息为实验数据,进行实验分析,结果表明,获取的偏好知识能更好地刻画学生画像.
In order to solve the problem that information overload causes students not to effectively acquire their prefe-rence knowledge,this paper proposes a method to obtain preference knowledge for the student profile.On the basis of the students'browsing content,the vector space model of student profile is established through the following two dimensions:keyword and topic distribution.Based on this,the similarity between students and knowledge is calculated to obtain direct prefe-rence knowledge.Subsequently,students'browsing knowledge content is applied to conduct cluster analysis,and the algorithm is designed according to students'learning behaviors to obtain indirect preference knowledge.The learning behavior information extracted from the actual operating system is taken as experimental data.Experimental results reveal that the acquired prefe-rence knowledge can better depict the student profile.
作者：
王晓东 江培超 李梦莹 郝明丽 胡富珍
Wang Xiaodong;Jiang Peichao;Li Mengying;Hao Mingli;Hu Fuzhen(College of Computer and Information Engineering,Big Data Engineering Lab of Teaching Resources&Assessment of Education Quality,Henan Province,Henan Normal University,Xinxiang 453007,China)
机构地区：
betway官方app 计算机与信息工程学院
出处：
《betway官方app 学报：自然科学版》 CAS 北大核心 2020年第6期19-24,共6页
基金：
横向研究项目(5201119160001,5202069169001) betway官方app 研究生科研创新项目(YL201917).
关键词：
学生画像 偏好知识 学习行为
student profile preference knowledge learning behavior
分类号：
TP399 [自动化与计算机技术—计算机应用技术]
