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Metadata quality in learning object repositories: a case study.

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Title: Metadata quality in learning object repositories: a case study.
Authors: Palavitsinis, Nikolaos, Manouselis, Nikos, Sanchez-Alonso, Salvador
Source: Electronic Library. 2014, Vol. 32 Issue 1, p62-82. 21p.
Subject Terms: *Information resources management, *Information retrieval, *Metadata, *Teaching aids, Agriculture, Research funding, Electronic publications
Geographic Terms: Europe
Abstract: Purpose – This paper aims to address the issue of poor quality of metadata records describing educational content in Learning Object Repositories (LORs). Through this, it aims to improve the discoverability of learning objects in such LORs through a structured process that supports metadata creation. Design/methodology/approach – This paper presents a proposed metadata quality assessment certification process for LORs. The process was designed as a generic approach that may be customized to fit various application domains. Findings – Initial results from the application of the process in the context of a specific LOR report an improvement of the quality of about 11,000 metadata records. More specifically, metadata completeness for all metadata elements used in the repository under examination was significantly improved from 30 percent to 85 percent. Research limitations/implications – The main limitation of the findings is that they come from the application of the proposed process on a relatively small repository, which does not allow safe generalizations without further experimental study in bigger ones where resources and requirements scale up. Practical implications – This paper addresses implications for the development of a repository in the educational domain, identifying issues related to the metadata application profile, the support to domain experts and the mechanisms that may be put in place to support metadata creation. Originality/value – The value and also the originality of the approach presented lies within the fact the proposed approach quantifies issues related to metadata creation and management by studying actions and perceptions of stakeholders who are involved in the repository lifecycle. [ABSTRACT FROM AUTHOR]
Copyright of Electronic Library is the property of Emerald Publishing Limited and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Education Research Complete
FullText Text:
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PubType: Academic Journal
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  Data: Metadata quality in learning object repositories: a case study.
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  Data: <searchLink fieldCode="AR" term="%22Palavitsinis%2C+Nikolaos%22">Palavitsinis, Nikolaos</searchLink><br /><searchLink fieldCode="AR" term="%22Manouselis%2C+Nikos%22">Manouselis, Nikos</searchLink><br /><searchLink fieldCode="AR" term="%22Sanchez-Alonso%2C+Salvador%22">Sanchez-Alonso, Salvador</searchLink>
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  Data: <searchLink fieldCode="JN" term="%22Electronic+Library%22">Electronic Library</searchLink>. 2014, Vol. 32 Issue 1, p62-82. 21p.
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  Data: *<searchLink fieldCode="DE" term="%22Information+resources+management%22">Information resources management</searchLink><br />*<searchLink fieldCode="DE" term="%22Information+retrieval%22">Information retrieval</searchLink><br />*<searchLink fieldCode="DE" term="%22Metadata%22">Metadata</searchLink><br />*<searchLink fieldCode="DE" term="%22Teaching+aids%22">Teaching aids</searchLink><br /><searchLink fieldCode="DE" term="%22Agriculture%22">Agriculture</searchLink><br /><searchLink fieldCode="DE" term="%22Research+funding%22">Research funding</searchLink><br /><searchLink fieldCode="DE" term="%22Electronic+publications%22">Electronic publications</searchLink>
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  Label: Abstract
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  Data: Purpose – This paper aims to address the issue of poor quality of metadata records describing educational content in Learning Object Repositories (LORs). Through this, it aims to improve the discoverability of learning objects in such LORs through a structured process that supports metadata creation. Design/methodology/approach – This paper presents a proposed metadata quality assessment certification process for LORs. The process was designed as a generic approach that may be customized to fit various application domains. Findings – Initial results from the application of the process in the context of a specific LOR report an improvement of the quality of about 11,000 metadata records. More specifically, metadata completeness for all metadata elements used in the repository under examination was significantly improved from 30 percent to 85 percent. Research limitations/implications – The main limitation of the findings is that they come from the application of the proposed process on a relatively small repository, which does not allow safe generalizations without further experimental study in bigger ones where resources and requirements scale up. Practical implications – This paper addresses implications for the development of a repository in the educational domain, identifying issues related to the metadata application profile, the support to domain experts and the mechanisms that may be put in place to support metadata creation. Originality/value – The value and also the originality of the approach presented lies within the fact the proposed approach quantifies issues related to metadata creation and management by studying actions and perceptions of stakeholders who are involved in the repository lifecycle. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Electronic Library is the property of Emerald Publishing Limited and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=ehh&AN=94621912
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        Value: 10.1108/EL-12-2011-0175
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              Text: 2014
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