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.

The use of computers and the Internet in the testing community has expanded the opportunity for innovative testing. Until now, there was no one source that reviewed the latest methods of automated scoring for complex assessments. This is the first volume to provide that coverage, along with examples of "best practices" in the design, implementation, and evaluation of automated complex assessment. The contributing authors, all noted leaders in the field, introduce each method in the context of actual applications in real assessments so as to provide a realistic view of current industry practices. Evidence Centered Design, an innovative approach to assessment design, is used as the book's conceptual framework. The chapters review both well known methods for automated scoring such as rule-based logic, regression-based, and IRT systems, as well as more recent procedures such as Bayesian and neural networks. The concluding chapters compare and contrast the various methods and provide a vision for the future. Each chapter features a discussion of the philosophical and practical approaches of the method, the associated implications for validity, reliability, and implementation, and the calculations and processes of each technique. Intended for researchers, practitioners, and advanced students in educational testing and measurement, psychometrics, cognitive science, technical training and assessment, diagnostic, licensing, and certification exams, and expert systems, the book also serves as a resource in advanced courses in educational measurement or psychometrics.

The use of computers and the Internet in the testing community has expanded the opportunity for innovative testing. Until now, there was no one source that reviewed the latest methods of automated scoring for complex assessments. This is the first volume to provide that coverage, along with examples of "best practices" in the design, implementation, and evaluation of automated complex assessment. The contributing authors, all noted leaders in the field, introduce each method in the context of actual applications in real assessments so as to provide a realistic view of current industry practices. Evidence Centered Design, an innovative approach to assessment design, is used as the book's conceptual framework. The chapters review both well known methods for automated scoring such as rule-based logic, regression-based, and IRT systems, as well as more recent procedures such as Bayesian and neural networks. The concluding chapters compare and contrast the various methods and provide a vision for the future. Each chapter features a discussion of the philosophical and practical approaches of the method, the associated implications for validity, reliability, and implementation, and the calculations and processes of each technique. Intended for researchers, practitioners, and advanced students in educational testing and measurement, psychometrics, cognitive science, technical training and assessment, diagnostic, licensing, and certification exams, and expert systems, the book also serves as a resource in advanced courses in educational measurement or psychometrics.

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.