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Analysis of Felder-Silverman Index of Learning Styles by a Data-Driven Statistical Approach
San Diego, CA December 11-December 13
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ISM.2006.30Eighth IEEE International Symposium o ...
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Silvia Rita Viola, Universita' Politecnica delle Marche, Italy
Sabine Graf, Vienna University of Technology, Austria
Kinshuk, Athabasca University, Canada
Tommaso Leo, Universita' Politecnica delle Marche, Italy
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.

Citation:
Silvia Rita Viola, Sabine Graf, Kinshuk , Tommaso Leo, "Analysis of Felder-Silverman Index of Learning Styles by a Data-Driven Statistical Approach," ism, pp.959-964, Eighth IEEE International Symposium on Multimedia (ISM'06), 2006
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