Multi-Criteria Approach to Learning Object Selection Through Fuzzy AHP

Ince M., Isik A. H., YİĞİT T.

JOURNAL OF MULTIPLE-VALUED LOGIC AND SOFT COMPUTING, vol.27, no.1, pp.47-62, 2016 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 27 Issue: 1
  • Publication Date: 2016
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.47-62
  • Süleyman Demirel University Affiliated: Yes


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.