Teaching resources recommendation system for K12 education

doi:10.11772/j.issn.1001-9081.2014.11.3353

Journal of Computer Applications ›› 2014, Vol. 34 ›› Issue (11): 3353-3356.DOI: 10.11772/j.issn.1001-9081.2014.11.3353

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Teaching resources recommendation system for K12 education

ZHANG Haidong¹,²,NI Wancheng¹,²,ZHAO Meijing¹,²,YANG Yiping²

1. CASIA-HHT Joint Laboratory of Smart Education, Beijing 100190, China
2. Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China;

Received:2014-05-21 Revised:2014-07-02 Online:2014-12-01 Published:2014-11-01
Contact: NI Wancheng

面向基础教育阶段的教学资源推荐系统

张海东¹,²,倪晚成¹,²,赵美静¹,²,杨一平¹

1. 中国科学院自动化研究所,北京100190;
2. 中科院自动化所—鸿合科技智能教育联合实验室, 北京100190

通讯作者: 倪晚成
作者简介:
张海东(1988-),男,河北邯郸人,博士研究生,CCF会员,主要研究方向:推荐系统、数据挖掘;倪晚成(1978-),女,四川邛崃人,高级工程师,博士,CCF会员,主要研究方向:资源共享系统、企业信息化;赵美静(1985-),女,河北廊坊人,博士研究生,主要研究方向:语义信息处理、自然语言处理;杨一平(1962-),男,江苏溧阳人,研究员,主要研究方向:语义信息处理、智能系统、虚拟现实。

Abstract

Abstract:

In data layer, the course model and resource model were built based on Markov chain and vector space model, and the teacher model was built based on teachers' personal registration information and nodes of course model. In off-line layer, the content features of course model and resource model were extracted via Term Frequency-Inverse Document Frequency (TF-IDF) algorithm, and the course model and resource model of data layer were initialized and optimized. Then relations between any two resources or recourse and course were calculated using association rules mining and similarity measure, and intermediate recommendation results were given using teacher model and course model. A weighted hybrid recommendation algorithm was proposed to generate recommendation list in on-line layer. The proposed system has been successfully applied in a real education resources sharing platform which consists of 600 thousand teaching resources.

摘要：

针对传统推荐方法应用于教学场景存在数据稀疏、缺乏对课程内容和教师上下文环境分析的问题,设计了一种面向基础教育阶段的网络教学资源推荐系统。该系统由数据层、离线层和在线层组成:1)数据层基于马尔可夫链和向量空间模型构建课程模型和资源模型,综合教师个人注册信息和课程模型的节点构建教师模型;2)离线层使用词频逆向文件频率(TF-IDF)算法提取课程和资源的内容特征,初始化并优化数据层的课程模型和资源模型,进一步应用关联规则挖掘和相似度量方法,计算任意两资源或课程与资源之间的关系,并结合课程模型推理教师模型,产生用于推荐的中间结果;3)在线层采用加权混合的方式产生推荐资源列表。该系统现已应用于某教育资源共享平台中,可实现对其中60万条资源的个性化推荐。

CLC Number:

ZHANG Haidong NI Wancheng ZHAO Meijing YANG Yiping. Teaching resources recommendation system for K12 education[J]. Journal of Computer Applications, 2014, 34(11): 3353-3356.

张海东倪晚成赵美静杨一平. 面向基础教育阶段的教学资源推荐系统[J]. 计算机应用, 2014, 34(11): 3353-3356.

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Teaching resources recommendation system for K12 education

面向基础教育阶段的教学资源推荐系统

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