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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 assessment of science
inquiry in genetics. This book is both a resource for professionals
interested in assessment and advanced students. Its clear
exposition, worked-through numerical examples, and demonstrations
from real and didactic applications provide invaluable
illustrations of how to use Bayes nets in educational assessment.
Exercises follow each chapter, and the online companion site
provides a glossary, data sets and problem setups, and links to
computational resources.
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 assessment of science
inquiry in genetics. This book is both a resource for professionals
interested in assessment and advanced students. Its clear
exposition, worked-through numerical examples, and demonstrations
from real and didactic applications provide invaluable
illustrations of how to use Bayes nets in educational assessment.
Exercises follow each chapter, and the online companion site
provides a glossary, data sets and problem setups, and links to
computational resources.
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