| dc.creator |
Ince, Murat |
|
| dc.creator |
YİĞİT, Tuncay |
|
| dc.creator |
Isik, Ali Hakan |
|
| dc.date |
2018-12-31T21:00:00Z |
|
| dc.date.accessioned |
2020-10-06T09:48:40Z |
|
| dc.date.available |
2020-10-06T09:48:40Z |
|
| dc.identifier |
41f0f144-010e-4a64-bc40-6ee2d2a57731 |
|
| dc.identifier |
10.1007/s00521-017-3023-7 |
|
| dc.identifier |
https://avesis.sdu.edu.tr/publication/details/41f0f144-010e-4a64-bc40-6ee2d2a57731/oai |
|
| dc.identifier.uri |
http://acikerisim.sdu.edu.tr/xmlui/handle/123456789/58468 |
|
| dc.description |
A wide variety of demand in e-learning and web-based learning caused a new approach in e-content presentation. In order to accomplish these demands, learning object repositories (LORs) were developed. LORs have many learning objects (LOs) that are used to produce different types of e-content. When there are many LOs in LORs, the evaluation and selection of them become a subjective and time-consuming process. Thus, selecting the most suitable and best qualified LO is considered as a multi-criteria decision-making (MCDM) problem. In this study, a hybrid analytic hierarchy process genetic algorithm (AHP-GA) method was developed for the evaluation of LOs from web-based Intelligent Learning Object Framework (Zonesa) LOR. This proposed hybrid system was used in a real case study and the results demonstrated that the proposed system can be used effectively by both users and machines to produce content by the help of LO metadata. |
|
| dc.language |
eng |
|
| dc.rights |
info:eu-repo/semantics/closedAccess |
|
| dc.title |
A hybrid AHP-GA method for metadata-based learning object evaluation |
|
| dc.type |
info:eu-repo/semantics/article |
|