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Journal cover: Multicultural Education & Technology Journal

Multicultural Education & Technology Journal

ISSN: 1750-497X

Online from: 2007

Subject Area: Education

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Quantitative online student profiling to forecast academic outcome from learning styles using dendrogram decision models


Document Information:
Title:Quantitative online student profiling to forecast academic outcome from learning styles using dendrogram decision models
Author(s):Kenneth D. Strang, (University of Technology Sydney, Sydney, Australia)
Citation:Kenneth D. Strang, (2008) "Quantitative online student profiling to forecast academic outcome from learning styles using dendrogram decision models", Multicultural Education & Technology Journal, Vol. 2 Iss: 4, pp.215 - 242
Keywords:Internet, Learning styles, Performance levels, Students
Article type:Research paper
DOI:10.1108/17504970810911043 (Permanent URL)
Publisher:Emerald Group Publishing Limited
Abstract:

Purpose – The purpose of this paper is to inform international student study strategies as well as course design and instructional approach.

Design/methodology/approach – Multiple research methods are applied, starting with exploratory data analysis, principal component analysis, confirmatory ordinal factor analysis, then recursive regression.

Findings – The meta-cognitive impacts of international learning styles on academic performance over two courses are proven.

Research limitations/implications – The learning styles of multicultural university students are assessed using an online a priori instrument to determine predictive impact on academic performance across different courses.

Practical implications – The implications of the dendrogram models are briefly explained with respect to student counselling, student study strategies and teaching approaches. The findings are discussed with respect to rival learning style theories and to appease criticisms of meta-analysis reviews.

Originality/value – Several statistically significant models were created including varimax and promax rotation solutions from ordinal factor analysis, as well as item response and latent factor dendrograms from recursive regression.



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