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Recovering a Probabilistic Knowledge Structure by Constraining its Parameter Space

Published online by Cambridge University Press:  01 January 2025

Luca Stefanutti  and
Egidio Robusto
Luca Stefanutti*
Affiliation:
University of Padua
Egidio Robusto
Affiliation:
University of Padua
*
Requests for reprints should be sent to Luca Stefanutti, Dipartimento di Psicologia Generale, University of Padua, Padua, Italy. E-mail: luca.stefanutti@unipd.it
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Abstract

In the Basic Local Independence Model (BLIM) of Doignon and Falmagne (Knowledge Spaces, Springer, Berlin, 1999), the probabilistic relationship between the latent knowledge states and the observable response patterns is established by the introduction of a pair of parameters for each of the problems: a lucky guess probability and a careless error probability. In estimating the parameters of the BLIM with an empirical data set, it is desirable that such probabilities remain reasonably small. A special case of the BLIM is proposed where the parameter space of such probabilities is constrained. A simulation study shows that the constrained BLIM is more effective than the unconstrained one, in recovering a probabilistic knowledge structure.

Keywords

probabilistic knowledge structuresbasic local independence modelconstrained parameter estimation
Type
Theory and Methods
Information
Psychometrika , Volume 74 , Issue 1 , March 2009 , pp. 83 - 96
Copyright
Copyright © 2008 The Psychometric Society

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