Multiple choice results are inherently probabilistic outcomes, as correct responses reflect a combination of knowledge and guessing, while incorrect responses additionally reflect blunder, a confidently committed mistake. To objectively resolve knowledge from responses in an MC test structure, we evaluated probabilistic models that explicitly…

Explored procedures to detect test takers using item preknowledge in computerized adaptive testing and suggested a Bayesian posterior log odds ratio index for this purpose. Simulation results support the use of the odds ratio index. (SLD)

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

With the increased use of computerized adaptive testing, which allows for continuous testing, new concerns about test security have evolved, one being the assurance that items in an item pool are safeguarded from theft. In this paper, the risk of score inflation and procedures to detect test takers using item preknowledge are explored. When test…

Bayesian networks are frequently used in educational assessments primarily for learning about students' knowledge and skills. There is a lack of works on assessing fit of Bayesian networks. This article employs the posterior predictive model checking method, a popular Bayesian model checking tool, to assess fit of simple Bayesian networks. A…