By Russell G. Almond, Robert J. Mislevy, Linda S. Steinberg, Duanli Yan, David M. Williamson
Bayesian inference networks, a synthesis of data and professional platforms, have complex reasoning lower than uncertainty in medication, company, and social sciences. This leading edge quantity is the 1st accomplished remedy exploring how they are often utilized to layout and research leading edge academic assessments.
Part I develops Bayes nets’ foundations in evaluation, information, and graph thought, and works throughout the real-time updating set of rules. half II addresses parametric kinds to be used with evaluate, model-checking recommendations, and estimation with the EM set of rules and Markov chain Monte Carlo (MCMC). a different characteristic is the volume’s grounding in Evidence-Centered layout (ECD) framework for evaluation layout. This “design ahead” procedure permits designers to take complete benefit of Bayes nets’ modularity and talent to version advanced evidentiary relationships that come up from functionality in interactive, technology-rich tests resembling simulations. half III describes ECD, situates Bayes nets as an fundamental section of a principled layout approach, and illustrates the tips with an in-depth examine the BioMass venture: An interactive, standards-based, web-delivered demonstration evaluation of technology inquiry in genetics.
This booklet is either a source for pros drawn to review and complex scholars. Its transparent exposition, worked-through numerical examples, and demonstrations from actual and didactic purposes offer valuable illustrations of the way to exploit Bayes nets in academic evaluation. workouts persist with every one bankruptcy, and the web better half website presents a thesaurus, information units and challenge setups, and hyperlinks to computational assets.
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Additional resources for Bayesian Networks in Educational Assessment
Furthermore, Bayes net can assess higher-level skills (such as science inquiry skills in Biomass) in ways that obtain evidence about lower-level skills, and partialling it out to understand what can be learned about the higher-level skills. Bayes nets provide real-time diagnosis. Because Bayes nets provide proﬁle information quickly, they can be queried at any time in an assessment situation. In particular, an intelligent tutoring system can use Bayes nets to make decisions about when to continue assessment, when to switch to instruction and what instruction would be expected to provide the most value.
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 . 1 . . . . . . . . . . . 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 .
559 Initial conditional distribution for observable 1 of Task 4 . . . 560 Summary statistics of parameter prior distributions . . . . . . 562 Observed responses . . . . . . . . . . . . . . . . . . . . 564 Summary statistics of prior and posterior population parameter distributions . . . . . . . . . . . . . . . . . . 567 Summary statistics of item parameter distributions . . . . . . 568 Prior and posterior expected proﬁciency levels .