Titlebook: Bayesian Networks in Educational Assessment; Russell G. Almond,Robert J. Mislevy,David M. Willi Textbook 2015 Springer Science+Business Me

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Explanation and Test Construction basis of those estimates. The quantity defined as the weight of evidence associated with a task is useful for explanation, as well as debugging models (explaining unexpected results). Expected weight of evidence is useful for assembling assessments either adaptively or in fixed forms.

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An Illustrative Examplehe network, learning the parameters of Bayes nets given a body of assessment data, and evaluating how well a proposed network fits the data. This chapter reviews these concepts in terms of an example: the mixed number subtraction example of Tatsuoka (1983).

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The Biomass Measurement ModelFrom one perspective, evidence-centered assessment design and Bayesian networks are just notations. In particular, it is easy to express familiar assessment design patterns using these notations. Bayesian networks are just a way to parameterize multidimensional latent class models. What have we gained?

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Mariusz Duplaga,Krzysztof Zielińskis used in discrete Bayes nets applications for assessment. It describes how, with the help of Bayes net software, to build and use Bayesian networks as the scoring engine for an assessment. It also illustrates some simple design patterns for building conditional probability tables.

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Bidesh Chakraborty,Mamata Daluihe network, learning the parameters of Bayes nets given a body of assessment data, and evaluating how well a proposed network fits the data. This chapter reviews these concepts in terms of an example: the mixed number subtraction example of Tatsuoka (1983).

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