Bayesian Networks in Educational Assessment by Russell G. Almond, Robert J. Mislevy, Linda S. Steinberg,

By Russell G. Almond, Robert J. Mislevy, Linda S. Steinberg, Duanli Yan, David M. Williamson

Bayesian inference networks, a synthesis of records and specialist platforms, have complex reasoning below uncertainty in medication, enterprise, and social sciences. This cutting edge quantity is the 1st finished remedy exploring how they are often utilized to layout and study leading edge academic assessments.
Part I develops Bayes nets’ foundations in evaluation, information, and graph conception, 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 different characteristic is the volume’s grounding in Evidence-Centered layout (ECD) framework for review layout. This “design ahead” method allows 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 equivalent to simulations. half III describes ECD, situates Bayes nets as an essential section of a principled layout method, and illustrates the tips with an in-depth examine the BioMass venture: An interactive, standards-based, web-delivered demonstration evaluate of technology inquiry in genetics.
This ebook is either a source for execs drawn to evaluation and complicated scholars. Its transparent exposition, worked-through numerical examples, and demonstrations from actual and didactic functions supply worthy illustrations of ways to exploit Bayes nets in academic overview. workouts persist with each one bankruptcy, and the net significant other web site presents a thesaurus, facts units and challenge setups, and hyperlinks to computational assets.

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Additional resources for Bayesian Networks in Educational Assessment

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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 .

Increasingly, the end users of tests want more than just a single score to use for selection or placement. They are looking for a set of scores to help diagnose problems the examinee might be facing. 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.

However, the idea dates back to Bernoulli and other early developers of probability. This use of probability to represent states of knowledge is most often found in the Bayesian approaches to statistics (Chap. 3). It is a powerful concept which allows us to use probability theory to make complex inferences from uncertain and incomplete evidence. In complex situations, it can be difficult to calculate the probability of an event, especially if there are many dependencies. The solution is to draw a picture.

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