A model for personalized selection of a learning scenario depending on learning styles

Inga Zilinskiene, Saulius Preidys

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

1 Citation (Scopus)

Abstract

This paper deals with one of Technology Enhanced Learning (TEL) problems - the personalized selection of a learning scenario. Personalization is treated here as appropriateness of a learning scenario to preferences of a particular student, mainly, his/her learning style. This paper proposes an extended approach to modeling learning scenario selection based on preferences of a student's learning style. An ant colony optimization algorithm is modified and applied. In order to give a theoretical background the main conceptions of personalization, learning scenario and learning style are briefly presented. The aim of this paper is twofold. First, data mining technique to obtain a student's learning style is presented; second, a model for personalized selection of a learning scenario is proposed.

Original languageEnglish
Title of host publicationFrontiers in Artificial Intelligence and Applications
PublisherIOS Press
Pages347-360
Number of pages14
Volume249
ISBN (Print)9781614991601
DOIs
Publication statusPublished - 2013
Externally publishedYes

Publication series

NameFrontiers in Artificial Intelligence and Applications
Volume249
ISSN (Print)09226389

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Keywords

  • Learning scenario
  • personalization
  • student's preferences

ASJC Scopus subject areas

  • Artificial Intelligence

Cite this

Zilinskiene, I., & Preidys, S. (2013). A model for personalized selection of a learning scenario depending on learning styles. In Frontiers in Artificial Intelligence and Applications (Vol. 249, pp. 347-360). (Frontiers in Artificial Intelligence and Applications; Vol. 249). IOS Press. https://doi.org/10.3233/978-1-61499-161-8-347