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

Ince M., YİĞİT T., Isik A. H.

NEURAL COMPUTING & APPLICATIONS, vol.31, pp.671-681, 2019 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 31
  • Publication Date: 2019
  • Doi Number: 10.1007/s00521-017-3023-7
  • Journal Name: NEURAL COMPUTING & APPLICATIONS
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.671-681
  • Süleyman Demirel University Affiliated: Yes

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.