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  • 标题:Social Web Content Enhancement in a Distance Learning Environment: Intelligent Metadata Generation for Resources
  • 本地全文:下载
  • 作者:Andrés García-Floriano ; Ángel Ferreira-Santiago ; Cornelio Yáñez-Márquez
  • 期刊名称:The International Review of Research in Open and Distributed Learning
  • 印刷版ISSN:1492-3831
  • 出版年度:2017
  • 卷号:18
  • 期号:1
  • DOI:10.19173/irrodl.v18i1.2646
  • 语种:English
  • 出版社:AU Press
  • 摘要:Social networking potentially offers improved distance learning environments by enabling the exchange of resources between learners. The existence of properly classified content results in an enhanced distance learning experience in which appropriate materials can be retrieved efficiently; however, for this to happen, metadata needs to be present. As manual metadata generation is time-costly and often eschewed by the authors of the social web resources, automatic generation is a fertile area for research as several kinds of metadata, such as author or topic, can be generated or extracted from the contents of a document. In this paper we propose a novel metadata generation system aimed at automatically tagging distance learning resources. This system is based on a recently-created intelligent pattern classifier; specifically, it trains on a corpus of example documents and then predicts the topic of a new document based on its text content. Metadata is generated in order to achieve a better integration of the web resources with the social networks. Experimental results for a two-class problem are promising and encourage research geared towards applying this method to multiple topics.
  • 其他摘要:Social networking potentially offers improved distance learning environments by enabling the exchange of resources between learners. The existence of properly classified content results in an enhanced distance learning experience in which appropriate materials can be retrieved efficiently; however, for this to happen, metadata needs to be present. As manual metadata generation is time-costly and often eschewed by the authors of the social web resources, automatic generation is a fertile area for research as several kinds of metadata, such as author or topic, can be generated or extracted from the contents of a document. In this paper we propose a novel metadata generation system aimed at automatically tagging distance learning resources. This system is based on a recently-created intelligent pattern classifier; specifically, it trains on a corpus of example documents and then predicts the topic of a new document based on its text content. Metadata is generated in order to achieve a better integration of the web resources with the social networks. Experimental results for a two-class problem are promising and encourage research geared towards applying this method to multiple topics.
  • 关键词:social networking; distance learning; social web content; metadata generation; intelligent classification
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