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A hybrid AHP-GA method for metadata-based learning object evaluation

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Abstract

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.

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Acknowledgements

The authors wish to thank the Scientific and Technological Research Council of Turkey (TUBITAK) that supported this project financially with project number EEEAG 115E600.

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Authors and Affiliations

  1. Vocational School of Technical Sciences, University of Suleyman Demirel, Çünür West Campus, 32200, Isparta, Turkey

    Murat İnce

  2. Department of Computer Engineering, University of Suleyman Demirel, Çünür West Campus, 32200, Isparta, Turkey

    Tuncay Yiğit

  3. Department of Computer Engineering, University of Mehmet Akif Ersoy, İstiklal Campus, 15030, Burdur, Turkey

    Ali Hakan Işık

Authors
  1. Murat İnce
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  2. Tuncay Yiğit
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  3. Ali Hakan Işık
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Correspondence to Murat İnce.

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This study was funded by Scientific and Technological Research Council of Turkey (TUBITAK) (grant number EEEAG 115E600).

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The authors declare that they have no conflict of interest.

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İnce, M., Yiğit, T. & Işık, A.H. A hybrid AHP-GA method for metadata-based learning object evaluation. Neural Comput & Applic 31 (Suppl 1), 671–681 (2019). https://doi.org/10.1007/s00521-017-3023-7

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  • DOI: https://doi.org/10.1007/s00521-017-3023-7

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