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 medication, enterprise, and social sciences. This cutting edge quantity is the 1st complete therapy exploring how they are often utilized to layout and examine leading edge academic assessments.
Part I develops Bayes nets’ foundations in overview, facts, and graph concept, and works during the real-time updating set of rules. half II addresses parametric kinds to be used with evaluation, model-checking strategies, 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” process permits designers to take complete benefit of Bayes nets’ modularity and skill to version advanced evidentiary relationships that come up from functionality in interactive, technology-rich checks akin to simulations. half III describes ECD, situates Bayes nets as an crucial element of a principled layout method, and illustrates the information with an in-depth examine the BioMass venture: An interactive, standards-based, web-delivered demonstration review of technological know-how inquiry in genetics.
This e-book is either a source for pros attracted to evaluation and complicated scholars. Its transparent exposition, worked-through numerical examples, and demonstrations from genuine and didactic functions supply necessary illustrations of ways to exploit Bayes nets in academic evaluation. routines persist with each one bankruptcy, and the web significant other 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
10 . . . . . . 6 Skill requirements for fraction subtraction items . . . . . . . 374 Equivalence classes and evidence models . . . . . . . . . . 376 Summary statistics for binary-skills model . . . . . . . . . . 390 Selected student responses . . . . . . . . . . . . . . . . . 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 .
This is an emerging ﬁeld called cognitively diagnostic assessment (Leighton and Gierl 2007; Rupp et al. 2010). The “cognitive” part of this name indicates that scores are chosen to reﬂect a cognitive model of how students acquire skills (see Sect. 3). The “diagnostic” part of the name reﬂects a phenomenon that seeks to identify and provide remedy for some problem in a students’ state of proﬁciency. Such diagnostic scores can be used for a variety of purposes: as an adjunct to a high stakes test to help a candidate prepare, as a guidance tool to help a learner choose an appropriate instructional strategy, or even shaping instructions on the ﬂy in an intelligent tutoring system.
2002d), Biomass (Steinberg et al. 2003, Chaps. 14 and 15), NetPASS (Behrens et al. 2004), ACED (this book, Chaps. 7 and 13; Shute 2004; Shute et al. 2005; Shute et al. 2008), an alternative scoring method for ETS’s ICT Literacy assessment (Katz et al. 2004), and a game-based assessment called SimCityEDU (Mislevy et al. 2014). 3 Cognitive and Psychometric Science The HYDRIVE experience taught us many lessons. Among them was the amount of work required to build a diagnostic assessment that truly relates to variables learners and educators care about.