基于知识点层次图的个性化习题推荐算法  

Personalized exercises Recommendation algorithm based on Knowledge Hierarchical Graph, ReKHG

作　　者：蒋昌猛 冯筠[1] 孙霞[1] 陈静 张蕾[1] 冯宏伟[1] JIANG Changmeng;FENG Jun;SUN Xia;CHEN Jing;ZHANG Lei;FENG Hongwei(School of Information Science and Technology,Northwest University,Xi’an 710127,China)

机构地区：[1]西北大学信息科学与技术学院,西安710127

出　　处：《计算机工程与应用》2018年第10期229-235,共7页Computer Engineering and Applications

基　　金：陕西省自然科学基金(No.2014JQ8367);西北大学研究生教学改革项目(No.YJD15003;No.YJG14003);陕西省天地网技术重点实验室开放课题基金资助

摘　　要：针对海量习题带来的信息过载导致学习针对性不强、效率不高等问题,提出了基于知识点层次图的个性化习题推荐算法(a personalized exercises Recommendation algorithm based on Knowledge Hierarchical Graph,Re KHG)。借鉴课程知识点体系结构的特点,构建了表征知识点层次关系的权重图,该权重图有效反映知识点间的层次关系。根据学生对知识点的掌握情况,在知识点层次图的基础上提出了一种个性化习题推荐算法。该算法通过更新学生-知识点失分率矩阵,获取学生掌握薄弱的知识点,以此实现习题推荐。实验结果表明,Re KHG算法能够针对性给学生推荐适合的习题。Aiming at the low pertinence and efficiency problem caused by massive exercises,a personalized exercises Recommendation algorithm based on Knowledge Hierarchical Graph(ReKHG)is proposed.Firstly,in consideration of the characteristic of system architecture,a weight map is built,which characterizes the knowledge hierarchical relations.This map can reflect the hierarchical relationship among knowledges efficiently.Then,a personalized exercise recommendation algorithm is proposed according to students’mastery of knowledges and their hierarchical graph.The ReKHG algorithm acquires students’weakness of knowledge mastery by updating knowledge losing-rate matrix of students.Experimental results show that ReKHG algorithm can recommend appropriate exercises for the targeted students.

关 键 词：知识点 层次图 习题推荐 失分率矩阵 

分 类 号：TP391.7[自动化与计算机技术—计算机应用技术]