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Generating metadata for cyberlearning resources through information retrieval and meta-search.

The goal of this study was to propose novel cyberlearning resource-based scientific referential metadata for an assortment of publications and scientific topics, in order to enhance the learning experiences of students and scholars in a cyberinfrastructure-enabled learning environment. By using info...

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Published in:Journal of the American Society for Information Science & Technology Vol. 64; no. 4; pp. 771 - 787
Main Author: Liu, Xiaozhong
Format: equations & formulas pictorial research tables/charts Journal Article
Published: Wiley-Blackwell Apr2013
Online Access:View this record in EBSCOhost
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      dt: Apr2013
      vid: 64
      iid: 4
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      pub: Wiley-Blackwell
      place: Malden, Massachusetts
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        104254847
        86213587
        10.1002/asi.22744
        104254847
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        atl: Generating metadata for cyberlearning resources through information retrieval and meta-search.
      aug:
        au: Liu, Xiaozhong
        affil: School of Library and Information Science, Indiana University
      sug:
        subj:
          Learning Methods
          Science
          Metadata
          Education, Non-Traditional
          Information Retrieval
          Internet
          Human
          Teaching Materials
          HTML
          Motion Pictures
          Reference Tools
          Questionnaires
          Electronic Publishing
      ab: The goal of this study was to propose novel cyberlearning resource-based scientific referential metadata for an assortment of publications and scientific topics, in order to enhance the learning experiences of students and scholars in a cyberinfrastructure-enabled learning environment. By using information retrieval and meta-search approaches, different types of referential metadata, such as related Wikipedia pages, data sets, source code, video lectures, presentation slides, and (online) tutorials for scientific publications and scientific topics will be automatically retrieved, associated, and ranked. In order to test our method of automatic cyberlearning referential metadata generation, we designed a user experiment to validate the quality of the metadata for each scientific keyword and publication and resource-ranking algorithm. Evaluation results show that the cyberlearning referential metadata retrieved via meta-search and statistical relevance ranking can help students better understand the essence of scientific keywords and publications.
      pubtype: Academic Journal
      doctype:
        equations & formulas
        pictorial
        research
        tables/charts
        Journal Article
      ougenre: Article
    language: English
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