Analysis of felder-silverman index of learning styles by a data-driven statistical approach

Silvia Rita Viola, Sabine Graf, Kinshuk, Tommaso Leo

Research output: Chapter in Book/Report/Conference proceedingPublished Conference contributionpeer-review

33 Citations (Scopus)

Abstract

In this paper a data driven analysis of Felder-Silverman Index of Learning Styles (ILS) is given. Results, obtained by Multiple Correspondence Analysis and cross-validated by correlation analysis, show the consistent dependencies between some styles; some latent dimensions present in data, that are unexpected, are discussed. Results are then compared with the ones given by literature concerning validity and reliability of ILS questionnaire. Both the results and the comparisons show the effectiveness of data driven methods for patterns extraction even when unexpected dependencies are found and the importance of coherence and consistency of mathematical representation of data with respect to the methods selected for an effective, precise and accurate modeling.

Original languageEnglish
Title of host publicationISM 2006 - 8th IEEE International Symposium on Multimedia
Pages959-964
Number of pages6
DOIs
Publication statusPublished - 2006
EventISM 2006 - 8th IEEE International Symposium on Multimedia - San Diego, CA, United States
Duration: 11 Dec. 200613 Dec. 2006

Publication series

NameISM 2006 - 8th IEEE International Symposium on Multimedia

Conference

ConferenceISM 2006 - 8th IEEE International Symposium on Multimedia
Country/TerritoryUnited States
CitySan Diego, CA
Period11/12/0613/12/06

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