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

İNCE M., YİĞİT T., Işık A. H.

Neural Computing and Applications, cilt.31, ss.671-681, 2019 (SCI-Expanded, Scopus) identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 31
  • Basım Tarihi: 2019
  • Doi Numarası: 10.1007/s00521-017-3023-7
  • Dergi Adı: Neural Computing and Applications
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.671-681
  • Anahtar Kelimeler: Analytic hierarchy process, Genetic algorithm, Learning object selection, Metadata, Recommendation system, Repository
  • Isparta Uygulamalı Bilimler Üniversitesi Adresli: Evet

Özet

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.