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

Ghusoon Salim Basheer, Mohd Sharifuddin Ahmad, Yee Chong Tang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationIntelligent Information and Database Systems - 5th Asian Conference, ACIIDS 2013, Proceedings
Pages549-558
Number of pages10
EditionPART 2
DOIs
Publication statusPublished - 11 Mar 2013
Event5th Asian Conference on Intelligent Information and Database Systems, ACIIDS 2013 - Kuala Lumpur, Malaysia
Duration: 18 Mar 201320 Mar 2013

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 2
Volume7803 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other5th Asian Conference on Intelligent Information and Database Systems, ACIIDS 2013
CountryMalaysia
CityKuala Lumpur
Period18/03/1320/03/13

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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    Basheer, G. S., Ahmad, M. S., & Tang, Y. C. (2013). A conceptual multi-agent framework using ant colony optimization and fuzzy algorithms for learning style detection. In Intelligent Information and Database Systems - 5th Asian Conference, ACIIDS 2013, Proceedings (PART 2 ed., pp. 549-558). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7803 LNAI, No. PART 2). https://doi.org/10.1007/978-3-642-36543-0_56