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• W2607267352 abstract "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 cognitive models in such situations, in terms of both statistical machinery and actual instrument development. The method taps the knowledge of experts to provide initial estimates for the probabilistic relationships among the variables in a multivariate latent variable model and refines these estimates using Markov chain Monte Carlo (MCMC) procedures. This process is illustrated in the context of NetPASS, a complex simulation-based assessment in the domain of computer networking. We describe a parameterization of the relationships in NetPASS via an ordered polytomous item response model and detail the updating of the model with observed data via Bayesian statistical procedures ultimately being provided by Markov chain Monte Carlo estimation. Specifying and Refining a Complex Measurement Model Instruments in educational measurement have taken on a variety of forms ranging from the more familiar( e.g., multiple-choice formats) to the unique (e.g., computer simulation of a real-world application). Different formats yield different work products, for example, a scantron sheet with circles filled in, essays to be scored by raters, and portfolios. Though methods for drawing inferences from examinees’ work products to their knowledge, skills, and abilities exist for the more popular assessment instruments, new and innovative assessment instruments are often left needing inferential procedures to be developed individually. Nonstandard and complex tasks result in complex work products, and different combinations of knowledge and skill may be tapped in different tasks or subtasks. Drawing proper inferences in these situations requires models that accumulate and incorporate 1 We wish to thank David Williamson, Malcolm Bauer, Russell Almond, Duanli Yan, and Margaret Redman of Educational Testing Service and John Behrens and Sarah Demark of Cisco Learning Institute for their involvement with, and their support of our participation in, the NetPASS project." @default.
• W2607267352 created "2017-04-28" @default.
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• W2607267352 date "2004-01-01" @default.
• W2607267352 modified "2023-09-25" @default.
• W2607267352 title "Specifying and refining a measurement model for a simulation-based assessment" @default.
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