Multi-criteria approach to learning object selection through fuzzy AHP

İNCE M., Işik A. H., YİĞİT T.

Journal of Multiple-Valued Logic and Soft Computing, cilt.27, sa.1, ss.47-62, 2016 (SCI-Expanded, Scopus) identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 27 Sayı: 1
  • Basım Tarihi: 2016
  • Dergi Adı: Journal of Multiple-Valued Logic and Soft Computing
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.47-62
  • Anahtar Kelimeler: Fuzzy analytic hierarchy process, Learning object selection, Metadata, Repository, Software, Triangular fuzzy number
  • Isparta Uygulamalı Bilimler Üniversitesi Adresli: Hayır

Özet

E-content includes Learning Objects (LO) and metadata to provide sus-tainability, 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.