基于知识状态的个性化学习资源推荐方法

doi:10.13438/j.cnki.jdzk.2019.03.005

吉首大学学报（自然科学版）

基于知识状态的个性化学习资源推荐方法

翟域，徐朦，黄斌

（1．贵州民族大学人文科技学院大数据与信息工程学院，贵州贵阳 550001；2.贵州师范大学大数据与计算机科学学院，贵州贵阳 550001）

出版日期:2019-05-25 发布日期:2019-06-04
通讯作者: 黄斌（1971—），男，湖南怀化人，贵州师范大学大数据与计算机科学学院教授，博士，主要从事大数据、云计算、人工智能研究.
基金资助:
国家自然科学基金资助项目（61540052）；贵州省科技创新人才团队建设项目（[2016]5629）

Personalized Learning Resource Recommendation Based on Knowledge State

ZHAI Yu,XU Meng,HUANG Bin

(1.School of Big Data and Information Engineering,College of Humanities & Sciences,Guizhou Minzu University,Guiyang 550001,China;2.School of Big Data and Computer Science,Guizhou Normal University,Guiyang 550001,China)

Online:2019-05-25 Published:2019-06-04

摘要/Abstract

摘要：

针对现有的个性化学习资源推荐方法存在不能够从学习者的学习缺陷出发推荐学习资源的不足，提出一种基于知识状态的个性化学习资源推荐方法,它首先根据知识点之间的关联关系构建知识图谱,然后根据学习者知识状态进行推导生成待学习知识点向量,最后设计相似性迭代算法从学习资源库中匹配最适合学习者的学习资源.通过实验证明，该方法具有不错的推荐效果和性能.

关键词： 个性化学习, 知识状态, 学习资源推荐, 知识图谱

Abstract:

The existing learning resource recommendation can not offer personalized resources according to the individual learning proficiencies.For that disadvantage,a personalized learning resource recommendation method based on knowledge state is proposed.Firstly,the knowledge graph is constructed according to the relationship between knowledge points.Then,according to the learner's knowledge state,the knowledge point vector to be learned is generated.Finally,the similarity iterative algorithm is designed to match the learning resources that are most suitable for the learner.The experimental results show that the method has good recommendation effect and performance.

Key words: personalized learning, knowledge state, learning resource recommendation, knowledge graph

翟域，徐朦，黄斌. 基于知识状态的个性化学习资源推荐方法[J]. 吉首大学学报（自然科学版）, DOI: 10.13438/j.cnki.jdzk.2019.03.005.

ZHAI Yu,XU Meng,HUANG Bin. Personalized Learning Resource Recommendation Based on Knowledge State[J]. Journal of Jishou University（Natural Sciences Edition), DOI: 10.13438/j.cnki.jdzk.2019.03.005.

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基于知识状态的个性化学习资源推荐方法

Personalized Learning Resource Recommendation Based on Knowledge State

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