期刊文献+

数字学习资源推荐技术研究现状及趋势分析 被引量:6

Research Status and Trends on Digital Learning Resource Recommendation Technology
下载PDF
导出
摘要 海量的数字化学习资源带来的资源过载和资源迷航的问题。首先总结了数字化学习资源推荐的国内外研究现状,以及常用的推荐技术及应用,在分析谷歌搜索推出的知识图谱与图书情报学的区别的基础上,结合知识图谱在教学领域的应用和发展情况,并以网络教育平台为例提出了基于知识图谱的数字化学习资源推荐的流程,对知识图谱推荐的特点进行分析。基于知识图谱的推荐的方法能够构成知识点的体系结构,推荐有效和相关联资源,解决网络学习中知识碎片化和学习导航的问题,并且可视化地呈现和学习资源的推荐对提高学习兴趣有很大帮助。 Digital resources are widely accepted by learners for its convenience and fast speed , good sharing quality, innovative forms and other advantages , however, huge numbers of digital learning resources bring the problems of overload and resource confusion and in view of this , digital learning resource recommendation tech-nology solves the problem very well.The research status of digital learning resource recommendation , and the commonly used recommendation technology and application are summarized.Based on the analysis of the differ-ences between the Google knowledge map and library and information science , combining the development of knowledge map in the field of teaching , the procedure of digital learning resource recommendation based on the knowledge map is proposed by taking network education platform as an example , and the characteristics of the knowledge map are analyzed.The recommendation method based on the knowledge map recommendation can constitute knowledge systems , and recommend effective and associated resources , which solves problems of the information fragmentation and resource confusion , and meanwhile the visualized presentation and recommenda-tion of resources can help improve learners ’ learning interest.
作者 赵佳男 王楠
出处 《北京邮电大学学报(社会科学版)》 CSSCI 2014年第6期90-96,共7页 Journal of Beijing University of Posts and Telecommunications(Social Sciences Edition)
基金 北京市支持中央高校共建项目(YETP0470)
关键词 数字化学习资源 推荐技术 知识图谱 digital learning resource recommendation technology knowledge map
  • 相关文献

参考文献6

二级参考文献22

  • 1陈悦,刘则渊.悄然兴起的科学知识图谱[J].科学学研究,2005,23(2):149-154. 被引量:791
  • 2鲁松 白硕 等.文本中词语权重计算方法的改进[A]..2000 International Conference on Multilingual Information Processing[C].,2000.31~36.
  • 3Resnick P, et al.Grouplens: An open architecture for collaborative filtering of NetNews[A].Proceedings of CSCW 94[C], Chaple Hill, NC, 1994:175-186.
  • 4Yao Jung Yang, Chuni Wu. An attribute-based ant colony systemfor adaptive learning object recommendation[J]. Expert Systems with Applications, 2009,(36): 3034-3047.
  • 5Wu Y H, Chen Y C, Chen A L P. Enabling Personalized Recommendation on the Web Based on User Interests and Behaviors[J].In:Proceedings of the 11^th International Workshop on Research lssues in Data Engineering. Los Alamitos,CA:IEEE CS Press,2001:17-24.
  • 6Mladenic D.Text-learning and Related Intelligent Agents:a Surver[J].IEEE Intelligent Systems, 1999,14(4):44-54.
  • 7Pazzani M,Billsus D.Leaming and Revising User Profiles:The Identification of Interesting Web Sites[J]. Machine Learning,1997,27:313-331.
  • 8Carroll J,Rosson M.Paradox of the Active User[A].Interfacing Thought:Cagnitive Aspects of Human-computer Interaction:MIT Press, 1987:80-111.
  • 9Eirinaki M, Vazirgiannis M.Web Mining for Web Personalization[J].ACM Transactions on Internet Technology, 2003,3(1 ): 1-27.
  • 10Runkler T A, BezdekJ C. Web Mining with Relational Clustering[J].Elsevier Science, 2001-12.

共引文献97

同被引文献103

引证文献6

二级引证文献33

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部