摘要
为了提高在线学习平台教学资源推荐的有效性,通过数据挖掘方法对OBE教学资源进行特征提取,分别生成教学资源和用户个性推荐库,采用核典型相关分析算法对教学资源特征和用户个性特征进行分析,选择相关系数高的教学资源推荐给用户。实例仿真证明,相比于常用的教学资源推荐算法,本文算法的准确度更高,推荐资源更精准。
In order to improve the effectiveness of recommending teaching resources on online learning platform,features of OBE teaching resources are extracted by data mining method to generate teaching resources and user personality recommendation library.Kernel canonical correlation analysis algorithm is used to analyze the characteristics of teaching resources and user personality characteristics,and teaching resources with high correlation coefficient are selected and recommended to users.The simulation show that compared with the commonly used teaching resource recommendation algorithms,this algorithm is able to recommend resources with higher accuracy.
作者
吴昌钱
刘敏
WU Changqian;LIU Min(Department of Computer Science and Technology,Minnan Science and Technology University,QuanZhou 362000,China;School of Information,Southwest Petroleum University,Chengdu 610500,China)
出处
《辽宁科技大学学报》
CAS
2021年第1期62-66,共5页
Journal of University of Science and Technology Liaoning
基金
福建省教育科学“十三五”规划(FJJKCG20-014)
福建省本科教学改革项目(FBJG20200327)
福建省中青年科技类科研项目(JAT190931)。
关键词
教学资源推荐
典型相关分析
核函数
数据挖掘
recommendation of teaching resource
canonical correlation analysis
kernel function
data mining
