Analysis of Felder-Silverman Index of Learning Styles by a Data-Driven Statistical Approach

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

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