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

Simple item page
dc.citedby3
dc.contributor.authorBasheer G.S.en_US
dc.contributor.authorAhmad M.S.en_US
dc.contributor.authorTang A.Y.C.en_US
dc.contributor.authorid55614274300en_US
dc.contributor.authorid56036880900en_US
dc.contributor.authorid36806985400en_US
dc.date.accessioned2023-12-29T07:44:48Z
dc.date.available2023-12-29T07:44:48Z
dc.date.issued2013
dc.description.abstractThis 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.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1007/978-3-642-36543-0_56
dc.identifier.epage558
dc.identifier.issuePART 2
dc.identifier.scopus2-s2.0-84874597548
dc.identifier.spage549
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84874597548&doi=10.1007%2f978-3-642-36543-0_56&partnerID=40&md5=0250fadacb2db6fb57eaf9c45c0df09e
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/30132
dc.identifier.volume7803 LNAI
dc.pagecount9
dc.sourceScopus
dc.sourcetitleLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.subjecte-Learning
dc.subjectLearner Modeling
dc.subjectLearning Style
dc.subjectMulti-agent System
dc.subjectVARK Learning Style
dc.subjectAlgorithms
dc.subjectAnt colony optimization
dc.subjectDatabase systems
dc.subjectE-learning
dc.subjectFuzzy logic
dc.subjectFuzzy sets
dc.subjectIntelligent agents
dc.subjectMulti agent systems
dc.subjectAgent and multi-agent systems
dc.subjectAgent-based framework
dc.subjectE-learning systems
dc.subjectEducational assessment
dc.subjectEducational process
dc.subjectFuzzy algorithms
dc.subjectLearner modeling
dc.subjectLearner's profile
dc.subjectLearning Style
dc.subjectMulti agent system (MAS)
dc.subjectMulti-agent learning
dc.subjectMultiagent framework
dc.subjectNew dimensions
dc.subjectOn-line learning systems
dc.subjectRecent trends
dc.subjectSearch Algorithms
dc.subjectLearning systems
dc.titleA conceptual multi-agent framework using ant colony optimization and fuzzy algorithms for learning style detectionen_US
dc.typeConference Paperen_US
dspace.entity.typePublication
Files
Collections
SCOPUS