Robert J. Mislevy

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Probability-based inference in complex networks of interdependent variables is an active topic in statistical research, spurred by such diverse applications as forecasting, pedigree analysis, troubleshooting, and medical diagnosis. This paper concerns the role of Bayesian inference networks for updating student models in intelligent tutoring systems (ITSs).(More)
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As observations and student models become complex, educational assessments that exploit advances in technology and cognitive psychology can outstrip familiar testing models and analytic methods. Within the Portal conceptual framework for assessment design, Bayesian inference networks (BINs) record beliefs about students’ knowledge and skills, in light of(More)
Title of dissertation: POSTERIOR PREDICTIVE MODEL CHECKING FOR MULTIDIMENSIONALITY IN ITEM RESPONSE THEORY AND BAYESIAN NETWORKS Roy Levy, Doctor of Philosophy, 2006 Dissertation directed by: Professor Robert J. Mislevy Department of Measurement, Statistics & Evaluation If data exhibit a dimensional structure more complex than what is assumed, key(More)
Simulation environments make it possible for science and engineering students to learn to interact with complex systems. Putting these capabilities to effective use for learning and assessing learning requires more than a simulation environment alone. It requires a conceptual framework for the knowledge, skills, and ways of thinking that are meant to be(More)
A central challenge in using learning progressions (LPs) in practice is modeling the relationships that link student performance on assessment tasks to students’ levels on the LP. On the one hand, there is a progression of theoretically defined levels, each defined by a configuration of knowledge, skills, and/or abilities (KSAs). On the other hand, there(More)