It is a common practice in item response theory (IRT) to treat estimates of item parameters, say "B" circumflex, as if they were the known, true quantities, "B." However, ignoring the uncertainty associated with item parameters can lead to biases and over-confidence in subsequent inferences such as ability estimation,…

This article addresses issues in model building and statistical inference in the context of student modeling. The use of probability-based reasoning to explicate hypothesized and empirical relationships and to structure inference in the context of proportional reasoning tasks is discussed. Ideas are illustrated with an example concerning…

In many practical applications of item response theory, the parameters of overlapping subsets of test items are estimated from different samples of examinees. A linking procedure is then employed to place the resulting item parameter estimates onto a common scale. It is standard practice to ignore the uncertainty associated with the linking step…

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…

The basic equations of item response theory provide a foundation for inferring examinees' abilities and items' operating characteristics from observed responses. In practice, though, examinees will usually not have provided a response to every available item--for reasons that may or may not have been intended by the test administrator, and that…

The challenges of modeling students' performance in simulation-based assessments include accounting for multiple aspects of knowledge and skill that arise in different situations and the conditional dependencies among multiple aspects of performance in a complex assessment. This paper describes a Bayesian approach to modeling and estimating…

A method for drawing inferences from complex samples is based on Rubin's approach to missing data in survey research. Standard procedures for drawing such inferences do not apply when the variables of interest are not observed directly, but must be inferred from secondary random variables which depend on the variables of interest stochastically.…