| dc.creator |
Ince, Murat |
|
| dc.creator |
Isik, Ali Hakan |
|
| dc.creator |
YİĞİT, Tuncay |
|
| dc.date |
2015-12-31T22:00:00Z |
|
| dc.date.accessioned |
2020-10-06T10:14:34Z |
|
| dc.date.available |
2020-10-06T10:14:34Z |
|
| dc.identifier |
578f2828-66a3-4c56-8dac-89f878b01d5f |
|
| dc.identifier |
https://avesis.sdu.edu.tr/publication/details/578f2828-66a3-4c56-8dac-89f878b01d5f/oai |
|
| dc.identifier.uri |
http://acikerisim.sdu.edu.tr/xmlui/handle/123456789/60650 |
|
| dc.description |
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. |
|
| dc.language |
eng |
|
| dc.rights |
info:eu-repo/semantics/closedAccess |
|
| dc.title |
Multi-Criteria Approach to Learning Object Selection Through Fuzzy AHP |
|
| dc.type |
info:eu-repo/semantics/article |
|