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Multi-Criteria Approach to Learning Object Selection Through Fuzzy AHP.

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Title: Multi-Criteria Approach to Learning Object Selection Through Fuzzy AHP.
Authors: INCE, MURAT1 muratince@sdu.edu.tr, IŞIK, ALI HAKAN2, YIG¯IT, TUNCAY3
Source: Journal of Multiple-Valued Logic & Soft Computing. 2016, Vol. 27 Issue 1, p47-62. 16p. 1 Color Photograph, 3 Diagrams, 2 Charts, 4 Graphs.
Subjects: Mathematical models of learning, Mathematical models of decision making, Metadata, Internetworking, Fuzzy algorithms
Abstract: E-content includes Learning Objects (LO) and metadata to provide sustainability, reusability, and interoperability. In order to accomplish the requirements, massive numbers of LOs are produced for learning object repositories (LOR). A LO uses metadata together with a huge amount of criteria. Due to this reason, defining the best qualified LO according to the needs is a multi-criteria decision making (MCDM) problem. Moreover, finding the most appropriate LO is a difficult task whenever the some criteria do not precisely match metadata parameters. In this study, a fuzzy analytical hierarchy process (FAHP) based MCDM method is employed to find the most suitable LO through the web-based SDUNESA LOR software. The proposed approach provides a new perspective to LO selection problem using the FAHP method. The study is illustrated with a real-world case according to computer engineering preferences. It is shown with the results that FAHP technique finds suitable LOs with a minimum consistency ratio by means of metadata values. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Multiple-Valued Logic & Soft Computing is the property of Old City Publishing, Inc. 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: Engineering Source
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  Data: Multi-Criteria Approach to Learning Object Selection Through Fuzzy AHP.
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+Multiple-Valued+Logic+%26+Soft+Computing%22">Journal of Multiple-Valued Logic & Soft Computing</searchLink>. 2016, Vol. 27 Issue 1, p47-62. 16p. 1 Color Photograph, 3 Diagrams, 2 Charts, 4 Graphs.
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  Data: <searchLink fieldCode="DE" term="%22Mathematical+models+of+learning%22">Mathematical models of learning</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+models+of+decision+making%22">Mathematical models of decision making</searchLink><br /><searchLink fieldCode="DE" term="%22Metadata%22">Metadata</searchLink><br /><searchLink fieldCode="DE" term="%22Internetworking%22">Internetworking</searchLink><br /><searchLink fieldCode="DE" term="%22Fuzzy+algorithms%22">Fuzzy algorithms</searchLink>
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  Data: E-content includes Learning Objects (LO) and metadata to provide sustainability, reusability, and interoperability. In order to accomplish the requirements, massive numbers of LOs are produced for learning object repositories (LOR). A LO uses metadata together with a huge amount of criteria. Due to this reason, defining the best qualified LO according to the needs is a multi-criteria decision making (MCDM) problem. Moreover, finding the most appropriate LO is a difficult task whenever the some criteria do not precisely match metadata parameters. In this study, a fuzzy analytical hierarchy process (FAHP) based MCDM method is employed to find the most suitable LO through the web-based SDUNESA LOR software. The proposed approach provides a new perspective to LO selection problem using the FAHP method. The study is illustrated with a real-world case according to computer engineering preferences. It is shown with the results that FAHP technique finds suitable LOs with a minimum consistency ratio by means of metadata values. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Journal of Multiple-Valued Logic & Soft Computing is the property of Old City Publishing, Inc. 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.)
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        Text: English
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        PageCount: 16
        StartPage: 47
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      – SubjectFull: Mathematical models of learning
        Type: general
      – SubjectFull: Mathematical models of decision making
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      – SubjectFull: Metadata
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      – SubjectFull: Internetworking
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      – SubjectFull: Fuzzy algorithms
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              Text: 2016
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