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: firstname.lastname@example.org).
T. Yiğit is with the Computer Engineering Department, University of Suleyman Demirel, Isparta, 32200, Turkey (e-mail: email@example.com).
A. H. Işık is with the Computer Engineering Department, University of Mehmet Akif Ersoy, Burdur, 15030, Turkey (e-mail: firstname.lastname@example.org).
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.