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

• Published in: Neural computing & applications Vol. 31; no. Suppl 1; pp. 671 - 681
• Main Authors: İnce, Murat, Yiğit, Tuncay, Işık, Ali Hakan
• Format: Journal Article
• Language: English
• Published: London Springer London 01.01.2019; Springer Nature B.V
• 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; Metadata; Repository; Genetic algorithm; Analytic hierarchy process; Learning object selection; Recommendation system
• ISSN: 0941-0643; 1433-3058
• DOI: 10.1007/s00521-017-3023-7

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.