Clustering student skill set profiles in a unit hypercube using mixtures of multivariate betas

Dean, N. and Nugent, R. (2013) Clustering student skill set profiles in a unit hypercube using mixtures of multivariate betas. Advances in Data Analysis and Classification, 7(3), pp. 339-357. (doi: 10.1007/s11634-013-0149-z)

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<br>This paper presents a finite mixture of multivariate betas as a new model-based clustering method tailored to applications where the feature space is constrained to the unit hypercube. The mixture component densities are taken to be conditionally independent, univariate unimodal beta densities (from the subclass of reparameterized beta densities given by Bagnato and Punzo 2013). The EM algorithm used to fit this mixture is discussed in detail, and results from both this beta mixture model and the more standard Gaussian model-based clustering are presented for simulated skill mastery data from a common cognitive diagnosis model and for real data from the Assistment System online mathematics tutor (Feng et al 2009). The multivariate beta mixture appears to outperform the standard Gaussian model-based clustering approach, as would be expected on the constrained space. Fewer components are selected (by BIC-ICL) in the beta mixture than in the Gaussian mixture, and the resulting clusters seem more reasonable and interpretable.</br> <br>This article is in technical report form, the final publication is available at &id=doi:10.1007/s11634-013-0149-z</br>

Item Type:Articles
Additional Information:The final publication is available at Springer via
Keywords:Mixture Model Clustering, Multivariate Beta Densities, Skill Set Profiles, Unit Hypercube
Glasgow Author(s) Enlighten ID:Dean, Dr Nema
Authors: Dean, N., and Nugent, R.
Subjects:H Social Sciences > HA Statistics
College/School:College of Science and Engineering > School of Mathematics and Statistics > Statistics
Research Group:Statistical Methodology
Journal Name:Advances in Data Analysis and Classification
Journal Abbr.:ADAC
Publisher:Springer-Verlag Berlin Heidelberg
ISSN (Online):1862-5355
Copyright Holders:Copyright © 2013 Springer-Verlag Berlin Heidelberg
First Published:First Published in Advances in Data Analysis and Classification 7(3):339-357
Publisher Policy:Reproduced in accordance with the copyright policy of the publisher.

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