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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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发表于 2025-3-21 18:44:07 | 显示全部楼层 |阅读模式
期刊全称Bayesian Networks in Educational Assessment
影响因子2023Russell G. Almond,Robert J. Mislevy,David M. Willi
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发行地址Features exercises to make the material concrete.ECD portions of the book (Ch. 2, 12 & 13) build on work that was basis for the 2000 NCME award for Outstanding Technical Contribution to Educational Me
学科分类Statistics for Social and Behavioral Sciences
图书封面Titlebook: Bayesian Networks in Educational Assessment;  Russell G. Almond,Robert J. Mislevy,David M. Willi Textbook 2015 Springer Science+Business Me
影响因子.Bayesian inference networks, a synthesis of statistics and expert systems, have advanced reasoning under uncertainty in medicine, business, and social sciences. This innovative volume is the first comprehensive treatment exploring how they can be applied to design and analyze innovative educational assessments..Part I develops Bayes nets’ foundations in assessment, statistics, and graph theory, and works through the real-time updating algorithm. Part II addresses parametric forms for use with assessment, model-checking techniques, and estimation with the EM algorithm and Markov chain Monte Carlo (MCMC). A unique feature is the volume’s grounding in Evidence-Centered Design (ECD) framework for assessment design. This “design forward” approach enables designers to take full advantage of Bayes nets’ modularity and ability to model complex evidentiary relationships that arise from performance in interactive, technology-rich assessments such as simulations. Part III describes ECD,situates Bayes nets as an integral component of a principled design process, and illustrates the ideas with an in-depth look at the BioMass project: An interactive, standards-based, web-delivered demonstration
Pindex Textbook 2015
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发表于 2025-3-21 22:24:56 | 显示全部楼层
IntroductionThis book explores the implications of applying Bayesian networks to educational assessment. This approach supports complex models as needed in for diagnostic testing and constructed response or interactive tasks, but it is also compatible with the models and techniques that have developed in psychometrics over the past century.
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Textbook 2015l sciences. This innovative volume is the first comprehensive treatment exploring how they can be applied to design and analyze innovative educational assessments..Part I develops Bayes nets’ foundations in assessment, statistics, and graph theory, and works through the real-time updating algorithm.
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Bayesian Probability and Statistics: a Reviewal independence, which will form the basic building blocks of our models. Graphical representation of probability models. Random variables. Bayes‘ theorem as a paradigm for learning about unknown quantities.
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Basic Graph Theory and Graphical Modelsce of an assessment area will rarely be comfortable with mathematical notation for expressing their ideas. To work with them, the psychometrician needs a representation which is rigorous, but intuitive enough for the substantive experts to be comfortable.
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Parameters for Bayesian Network Modelss that add a layer of probabilistic noise to logical functions such as AND and OR gates, producing the kinds of link functions seen in cognitive diagnosis. Second are models that use functions from normal regression theory and item response theory (such as Samejima‘s graded response model) to model probability tables more parsimoniously.
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Luca Bartocci,Damiana Lucentinie mathematical models can be refined with data. Although throughout the book there are references to cognitive processes that the probability distributions model, the full discussion of assessment design follows the discussion of the more mathematical issues.
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