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

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-a...

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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
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id	my.uniten.dspace-393
record_format	dspace
spelling	my.uniten.dspace-3932017-07-30T22:14:56Z A conceptual multi-agent framework using ant colony optimization and fuzzy algorithms for learning style detection Basheer, G.S. Ahmad, M.S. Tang, A.Y.C. e-Learning Learner Modeling Learning Style Multi-agent System VARK Learning Style 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. 2017-07-25T03:42:09Z 2017-07-25T03:42:09Z 2013 Conference Paper https://www.scopus.com/inward/record.uri?eid=2-s2.0-84874597548&doi=10.1007%2f978-3-642-36543-0_56&partnerID=40&md5=0250fadacb2db6fb57eaf9c45c0df09e http://dspace.uniten.edu.my:8080/jspui/handle/123456789/393 10.1007/978-3-642-36543-0_56 2-s2.0-84874597548 en Lecture Notes in Computer Science
institution	Universiti Tenaga Nasional
building	UNITEN Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Tenaga Nasional
content_source	UNITEN Institutional Repository
url_provider	http://dspace.uniten.edu.my/
language	English
topic	e-Learning Learner Modeling Learning Style Multi-agent System VARK Learning Style
spellingShingle	e-Learning Learner Modeling Learning Style Multi-agent System VARK Learning Style Basheer, G.S. Ahmad, M.S. Tang, A.Y.C. A conceptual multi-agent framework using ant colony optimization and fuzzy algorithms for learning style detection
description	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.
format	Conference Paper
author	Basheer, G.S. Ahmad, M.S. Tang, A.Y.C.
author_facet	Basheer, G.S. Ahmad, M.S. Tang, A.Y.C.
author_sort	Basheer, G.S.
title	A conceptual multi-agent framework using ant colony optimization and fuzzy algorithms for learning style detection
title_short	A conceptual multi-agent framework using ant colony optimization and fuzzy algorithms for learning style detection
title_full	A conceptual multi-agent framework using ant colony optimization and fuzzy algorithms for learning style detection
title_fullStr	A conceptual multi-agent framework using ant colony optimization and fuzzy algorithms for learning style detection
title_full_unstemmed	A conceptual multi-agent framework using ant colony optimization and fuzzy algorithms for learning style detection
title_sort	conceptual multi-agent framework using ant colony optimization and fuzzy algorithms for learning style detection
publishDate	2017
url	http://dspace.uniten.edu.my:8080/jspui/handle/123456789/393
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score	13.222552

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

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