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Computer adaptive testing. --- Anthropology. --- Item response theory. --- Educational tests and measurements --- Psychological tests --- Psychometrics --- Human beings --- Adaptive testing, Computer --- CAT (Computer adaptive testing) --- Computer adaptive tests --- Computerized adaptive testing --- Ability --- Competency-based educational tests --- Testing --- Primitive societies --- Social sciences

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"The general theme of this book is to present the applications of artificial intelligence (AI) in test development. In particular, this book includes research and successful examples of using AI technology in automated item generation, automated test assembly, automated scoring, and computerized adaptive testing. By utilizing artificial intelligence, the efficiency of item development, test form construction, test delivery, and scoring could be dramatically increased. Chapters on automated item generation offer different perspectives related to generating a large number of items with controlled psychometric properties including the latest development of using machine learning methods. Automated scoring is illustrated for different types of assessments such as speaking and writing from both methodological aspects and practical considerations. Further, automated test assembly is elaborated for the conventional linear tests from both classical test theory and item response theory perspectives. Item pool design and assembly for the linear-on-the-fly tests elaborates more complications in practice when test security is a big concern. Finally, several chapters focus on computerized adaptive testing (CAT) at either item or module levels. CAT is further illustrated as an effective approach to increasing test-takers' engagement in testing. In summary, the book includes both theoretical, methodological, and applied research and practices that serve as the foundation for future development. These chapters provide illustrations of efforts to automate the process of test development. While some of these automation processes have become common practices such as automated test assembly, automated scoring, and computerized adaptive testing, some others such as automated item generation calls for more research and exploration. When new AI methods are emerging and evolving, it is expected that researchers can expand and improve the methods for automating different steps in test development to enhance the automation features and practitioners can adopt quality automation procedures to improve assessment practices" --

Artificial intelligence. Robotics. Simulation. Graphics --- Mathematical linguistics --- Examinations --- Educational tests and measurements --- Artificial intelligence --- Computer adaptive testing. --- Design and construction. --- Data processing. --- Educational applications. --- Adaptive testing, Computer --- CAT (Computer adaptive testing) --- Computer adaptive tests --- Computerized adaptive testing --- Ability --- Competency-based educational tests --- Education --- Test construction --- Test design --- Testing --- Data processing

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Psychological tests --- Computer adaptive testing --- Congresses --- 681.3*G3 --- 519.22 --- 796.012 --- -Mental tests --- Psychological assessment --- Tests, Psychological --- Psychology --- Testing --- Clinical psychology --- Educational tests and measurements --- Adaptive testing, Computer --- CAT (Computer adaptive testing) --- Computer adaptive tests --- Computerized adaptive testing --- Ability --- Competency-based educational tests --- Probability and statistics: probabilistic algorithms (including Monte Carlo);random number generation; statistical computing; statistical software (Mathematics of computing) --- Statistical theory. Statistical models. Mathematical statistics in general --- Movement and motor functions: tests and measurement --- Methodology --- Computer adaptive testing. --- Congresses. --- -Probability and statistics: probabilistic algorithms (including Monte Carlo);random number generation; statistical computing; statistical software (Mathematics of computing) --- 796.012 Movement and motor functions: tests and measurement --- 519.22 Statistical theory. Statistical models. Mathematical statistics in general --- 681.3*G3 Probability and statistics: probabilistic algorithms (including Monte Carlo);random number generation; statistical computing; statistical software (Mathematics of computing) --- -Adaptive testing, Computer --- Mental tests --- Psychological tests - Congresses

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The goal of this guide and manual is to provide a practical and brief overview of the theory on computerized adaptive testing (CAT) and multistage testing (MST) and to illustrate the methodologies and applications using R open source language and several data examples.  Implementation relies on the R packages catR and mstR that have been already or are being developed by the first author (with the team) and that include some of the newest research algorithms on the topic. The book covers many topics along with the R-code: the basics of R, theoretical overview of CAT and MST, CAT designs, CAT assembly methodologies, CAT simulations, catR package, CAT applications, MST designs, IRT-based MST methodologies, tree-based MST methodologies, mstR package, and MST applications.  CAT has been used in many large-scale assessments over recent decades, and MST has become very popular in recent years.  R open source language also has become one of the most useful tools for applications in almost all fields, including business and education.  Though very useful and popular, R is a difficult language to learn, with a steep learning curve.  Given the obvious need for but with the complex implementation of CAT and MST, it is very difficult for users to simulate or implement CAT and MST.  Until this manual, there has been no book for users to design and use CAT and MST easily and without expense; i.e., by using the free R software.  All examples and illustrations are generated using predefined scripts in R language, available for free download from the book's website. Provides exhaustive descriptions of CAT and MST processes in an R environment  Guides users to simulate and implement CAT and MST using R for their applications   Summarizes the latest developments and challenges of packages catR and mstR  Provides R packages catR and mstR and illustrates to users how to do CAT and MST simulations and implementations using R David Magis, PhD, is Research Associate of the “Fonds de la Recherche Scientifique – FNRS”  at the Department of Education, University of Liège, Belgium. His specialization is statistical methods in psychometrics, with special interest in item response theory, differential item functioning and computerized adaptive testing. His research interests include both theoretical and methodological development as well as open source implementation and dissemination in R. He is the main developer and maintainer of the packages catR and mstR, among others. Duanli Yan, PhD, is Manager of Data Analysis and Computational Research for Automated Scoring group in the Research and Development division at the Educational Testing Service (ETS).  She is also an Adjunct Professor at Rutgers University.  Dr. Yan has been the statistical coordinator for the EXADEP™ test, and the TOEIC® Institutional programs, a Development Scientist for innovative research applications, and a Psychometrician for several operational programs.  Dr. Yan received many awards, including the 2011 ETS Presidential Award, the 2013 NCME Brenda Lyod award, and the 2015 IACAT Early Career Award.  She is a co-editor for Computerized Multistage Testing: Theory and Applications and a co-author for Bayesian Networks in Educational Assessment.   Alina A. von Davier, PhD, is Senior Research Director of the Computational Psychometrics Research Center at Educational Testing Service (ETS) and an Adjunct Professor at Fordham University.  At ETS she leads the Computational Psychometrics Research Center, where she is responsible for developing a team of experts and a psychometric research agenda in support of next generation assessments.  Computational psychometrics, which include machine learning and data mining techniques, Bayesian inference methods, stochastic processes and psychometric models are the main set of tools employed in her current work.  She also works with psychometric models applied to educational testing: test score equating methods, item response theory models, and adaptive testing. .

Statistics. --- Assessment. --- Educational psychology. --- Education --- Psychometrics. --- Statistical Theory and Methods. --- Assessment, Testing and Evaluation. --- Statistics for Social Science, Behavorial Science, Education, Public Policy, and Law. --- Statistics and Computing/Statistics Programs. --- Educational Psychology. --- Psychology. --- Mathematical statistics. --- Educational tests and measuremen. --- Computer adaptive testing. --- R (Computer program language) --- GNU-S (Computer program language) --- Domain-specific programming languages --- Adaptive testing, Computer --- CAT (Computer adaptive testing) --- Computer adaptive tests --- Computerized adaptive testing --- Ability --- Competency-based educational tests --- Testing --- Statistics for Social Sciences, Humanities, Law. --- Psychology, Educational --- Psychology --- Child psychology --- Statistical analysis --- Statistical data --- Statistical methods --- Statistical science --- Mathematics --- Econometrics --- Measurement, Mental --- Measurement, Psychological --- Psychological measurement --- Psychological scaling --- Psychological statistics --- Psychometry (Psychophysics) --- Scaling, Psychological --- Psychological tests --- Scaling (Social sciences) --- Statistical inference --- Statistics, Mathematical --- Statistics --- Probabilities --- Sampling (Statistics) --- Measurement --- Scaling --- Methodology --- Statistics . --- Education—Psychology.