Modeling personalized learning styles in a web-based learning system

Chia Cheng Hsu, Kun Te Wang, Yueh-Min Huang

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

6 Citations (Scopus)


An innovative learning mechanism for identifying learners' learning styles to improve adaptive learning is proposed. Hypermedia-learning tools are highly interactive to learners in web-based environments that have become increasingly popular in the field of education. However, these learning tools are frequently inadequate for individualize learning because accessing adaptive learning content is required for learners to achieve objectives. For predicating adaptive learning, a neuron-fuzzy inference approach is used to model the diagnosis of learning styles. Then, according to the diagnosis results, a recommendation model is constructed to help learners obtain adaptive digital content. The proposed approach has the capability of tracking learning activities on-line to correspond with learning styles. The results show that the identified model successfully classified 102 learners into groups based on learning style. The implemented learning mechanism produced a clear learning guide for learning activities, which can help an advanced learning system retrieve a well-structure learning unit.

Original languageEnglish
Title of host publicationTransactions on Edutainment IV
Number of pages10
Publication statusPublished - 2010 Nov 8
Event5th International Conference on E-learning and Games, Edutainment 2010 - Changchun, China
Duration: 2010 Aug 12010 Aug 1

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume6250 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other5th International Conference on E-learning and Games, Edutainment 2010

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Theoretical Computer Science


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