By Russell G. Almond, Robert J. Mislevy, Linda S. Steinberg, Duanli Yan, David M. Williamson
Bayesian inference networks, a synthesis of information and professional platforms, have complex reasoning less than uncertainty in drugs, enterprise, and social sciences. This leading edge quantity is the 1st entire remedy exploring how they are often utilized to layout and study cutting edge academic assessments.
Part I develops Bayes nets’ foundations in evaluate, facts, and graph concept, and works throughout the real-time updating set of rules. half II addresses parametric types to be used with evaluate, model-checking options, and estimation with the EM set of rules and Markov chain Monte Carlo (MCMC). a special characteristic is the volume’s grounding in Evidence-Centered layout (ECD) framework for evaluation layout. This “design ahead” method allows designers to take complete good thing about Bayes nets’ modularity and talent to version advanced evidentiary relationships that come up from functionality in interactive, technology-rich exams resembling simulations. half III describes ECD, situates Bayes nets as an necessary portion of a principled layout approach, and illustrates the guidelines with an in-depth examine the BioMass undertaking: An interactive, standards-based, web-delivered demonstration review of technological know-how inquiry in genetics.
This booklet is either a source for execs attracted to overview and complex scholars. Its transparent exposition, worked-through numerical examples, and demonstrations from genuine and didactic purposes supply beneficial illustrations of ways to take advantage of Bayes nets in academic review. routines stick to every one bankruptcy, and the web spouse website offers a thesaurus, info units and challenge setups, and hyperlinks to computational assets.
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Additional resources for Bayesian Networks in Educational Assessment
260 Conditional probability table for simple graded response model . 263 Compensatory combination function and graded response link function . . . . . . . . . . . . . . . . . . . . . . . . . 8 . . . . . . . . . . . . . . . . . . . . . 47 267 Special cases of the generic measurement model . . . . . . . 285 Prior and posterior statistics for beta distribution with r successes in n trials . . . . . . . . . . . . . . . . . . . .
391 Prior and posterior probabilities for selected examinees . . . . 392 Summary statistics for binary-skills model, Admin 1 . . . . . 7 Summary statistics for binary-skills model, Admin 2 . . . . . 8 Item-ﬁt indices for the mixed-number subtraction test . . . . . 9 Person-ﬁt p-values for selected students . . . . . . . . . . . 12 Summary of the four processes . . . . . . . . . . . . . . . 469 Confusion matrix for binary proﬁciency and observable . . .
1 . . . . . . . . . . . 483 Three-way table of two observables given proﬁciency variable . 484 Three-way table of two observables given marginal proﬁciency . 485 Calculation of expected weight of evidence . . . . . . . . . . 4 . . . . . . . . . . . . . . . . . . 502 Ten randomly selected entries from a set of pretest data . . . . 502 Expected accuracy matrix for two experimental tasks . . . . . 503 Expected accuracy matrix (normalized) for two multiple-choice tasks .
Bayesian Networks in Educational Assessment by Russell G. Almond, Robert J. Mislevy, Linda S. Steinberg, Duanli Yan, David M. Williamson