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IJIET 2017 Vol.7(12): 884-887 ISSN: 2010-3689
doi: 10.18178/ijiet.2017.7.12.989

AHP-TOPSIS Method for Learning Object Metadata Evaluation

M. İnce, T. Yiğit, and A. H. Işık

Abstract—Increasing usage of computers in educational systems such as web based learning systems cause huge e-content needs. In this context, Learning Objects (LOs), stored in Learning Object Repositories (LORs), are used to produce e-content and other educational materials. Evaluation and selection of LOs are difficult and time consuming process when LO and their descriptive metadata numbers are high. Therefore, LO selection process is considered as a multi criteria decision making (MCDM) problem. In this study, analytic hierarchy process - technique for order of preference by similarity to ideal solution (AHP-TOPSIS) methods are combined for selection of LOs from web-based Intelligent Learning Object Framework LOR that is called ZONESA. The results of the system, used in a real case study, showed that the proposed system can be used effectively to produce appropriate content using LO metadata.

Index Terms—Analytic hierarchy process, learning object selection, metadata, topsis.

M. İnce is with the Vocational School of Technical Sciences, University of Suleyman Demirel, Isparta, 32200, Turkey (e-mail: muratince@sdu.edu.tr).
T. Yiğit is with the Computer Engineering Department, University of Suleyman Demirel, Isparta, 32200, Turkey (e-mail: tuncayyigit@sdu.edu.tr).
A. H. Işık is with the Computer Engineering Department, University of Mehmet Akif Ersoy, Burdur, 15030, Turkey (e-mail: ahakan@mehmetakif.edu.tr).

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Cite: M. İnce, T. Yiğit, and A. H. Işık, "AHP-TOPSIS Method for Learning Object Metadata Evaluation," International Journal of Information and Education Technology vol. 7, no. 12, pp. 884-887, 2017.

General Information

  • ISSN: 2010-3689 (Online)
  • Abbreviated Title: Int. J. Inf. Educ. Technol.
  • Frequency: Monthly
  • DOI: 10.18178/IJIET
  • Editor-in-Chief: Prof. Dr. Steve Thatcher
  • Managing Editor: Ms. Nancy Y. Liu
  • Abstracting/ Indexing: Scopus (CiteScore 2022: 2.0), INSPEC (IET), UGC-CARE List (India), CNKI, EBSCO, Google Scholar
  • E-mail: ijiet@ejournal.net

 

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