A hybrid AHP-GA method for metadata-based learning object evaluation
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 t...
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| Veröffentlicht in: | Neural computing & applications 2019-01, Vol.31 (Suppl 1), p.671-681 |
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| creator | İnce, Murat Yiğit, Tuncay Işık, Ali Hakan |
| 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. |
| doi_str_mv | 10.1007/s00521-017-3023-7 |
| format | Article |
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| subjects | Analytic hierarchy process Artificial Intelligence Computational Biology/Bioinformatics Computational Science and Engineering Computer Science Data Mining and Knowledge Discovery Decision analysis Decision making Distance learning Genetic algorithms Hybrid systems Image Processing and Computer Vision Metadata Multiple criterion Original Article Probability and Statistics in Computer Science Repositories |
| title | A hybrid AHP-GA method for metadata-based learning object evaluation |
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