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Modeling Diagnostic Assessments with Bayesian Networks
Author(s) -
Almond Russell G.,
DiBello Louis V.,
Moulder Brad,
ZapataRivera JuanDiego
Publication year - 2007
Publication title -
journal of educational measurement
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.917
H-Index - 47
eISSN - 1745-3984
pISSN - 0022-0655
DOI - 10.1111/j.1745-3984.2007.00043.x
Subject(s) - bayesian network , computer science , variable order bayesian network , machine learning , flexibility (engineering) , artificial intelligence , inference , bayesian statistics , bayesian probability , bayesian inference , set (abstract data type) , data mining , statistics , mathematics , programming language
This paper defines Bayesian network models and examines their applications to IRT‐based cognitive diagnostic modeling. These models are especially suited to building inference engines designed to be synchronous with the finer grained student models that arise in skills diagnostic assessment. Aspects of the theory and use of Bayesian network models are reviewed, as they affect applications to diagnostic assessment. The paper discusses how Bayesian network models are set up with expert information, improved and calibrated from data, and deployed as evidence‐based inference engines. Aimed at a general educational measurement audience, the paper illustrates the flexibility and capabilities of Bayesian networks through a series of concrete examples, and without extensive technical detail. Examples are provided of proficiency spaces with direct dependencies among proficiency nodes, and of customized evidence models for complex tasks. This paper is intended to motivate educational measurement practitioners to learn more about Bayesian networks from the research literature, to acquire readily available Bayesian network software, to perform studies with real and simulated data sets, and to look for opportunities in educational settings that may benefit from diagnostic assessment fueled by Bayesian network modeling.