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FUSE: A Fuzzy-Semantic Framework for Personalizing Learning Recommendations

Author

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  • Nguyen Dinh Hoa Cuong

    (Department of Computer Science, Faculty of Science, Khon Kaen University, Khon Kaen 40002, Thailand†Department of Business Information Systems, College of Economics, Hue University, Vietnam)

  • Ngamnij Arch-Int

    (Department of Computer Science, Faculty of Science, Khon Kaen University, Khon Kaen 40002, Thailand)

  • Somjit Arch-Int

    (Department of Computer Science, Faculty of Science, Khon Kaen University, Khon Kaen 40002, Thailand)

Abstract

The use of instructional Semantic Web rules to deliver personalized learning recommendations has become an emerging trend in intelligent tutoring systems (ITSs) because it enables experts’ domain knowledge to be easily transferred to machine-readable formats. However, many approaches to ITS design using instructional Semantic Web rules have evaluated learners’ performances without considering the uncertainty of the evaluation process or other factors such as learning behavior and speed of response. In this paper, we present an ITS framework named FUSE, which uses a novel recommendation-making mechanism based on a fuzzy-semantic reasoning process and a multiagent system. In this framework, fuzzy reasoning and semantic reasoning are integrated to form a unified reasoning process and provide personalized learning recommendations adaptively and semantically. A field experiment was conducted at a public university. The results indicated that FUSE enabled significant achievements in both enhancing learning performance and increasing learners’ participation in the learning process.

Suggested Citation

  • Nguyen Dinh Hoa Cuong & Ngamnij Arch-Int & Somjit Arch-Int, 2018. "FUSE: A Fuzzy-Semantic Framework for Personalizing Learning Recommendations," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 17(04), pages 1173-1201, July.
    • Handle: RePEc:wsi:ijitdm:v:17:y:2018:i:04:n:s0219622018500220
    DOI: 10.1142/S0219622018500220
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    References listed on IDEAS

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    1. Guangxu Li & Gang Kou & Changsheng Lin & Liang Xu & Yi Liao, 2015. "Multi-attribute decision making with generalized fuzzy numbers," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 66(11), pages 1793-1803, November.
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