Multi-Criteria Approach to Learning Object Selection Through Fuzzy AHP
Murat Ince, Ali Hakan Isik and Tuncay Yigit
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.
Keywords: Fuzzy analytic hierarchy process, learning object selection, metadata, repository, triangular fuzzy number, software