A conceptual multi-agent framework using ant colony optimization and fuzzy algorithms for learning style detection

• Main Authors: Basheer, G.S., Ahmad, M.S., Tang, A.Y.C.
• Format: Conference Paper
• Language: English
• Published: 2017
• Subjects: e-Learning; Learner Modeling; Learning Style; Multi-agent System; VARK Learning Style
• Online Access: http://dspace.uniten.edu.my:8080/jspui/handle/123456789/393

This paper examines the progress of researches that exploit multi-agent systems for detecting learning styles and adapting educational processes in e-Learning systems. In a summarized survey of the literature, we review and compile the recent trends of researches that applied and implemented multi-agent systems in educational assessment. We discuss both agent and multi-agent systems and focus on the implications of the theory of detecting learning styles that constitutes behaviors of learners when using online learning systems, learner's profile, and the structure of multi-agent learning systems. We propose a new dimension to detect learning styles, which involves the individuals of learners' social surrounding such as friends, parents, and teachers in developing a novel agent-based framework. The multi-agent system applies ant colony optimization and fuzzy logic search algorithms as tools to detecting learning styles. Ultimately, a working prototype will be developed to validate the framework using ant colony optimization and fuzzy logic. © 2013 Springer-Verlag.