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The arrival of the computer in educational and psychological testing has led to the current popularity of adaptive testing---a testing format in which the computer uses statistical information about the test items to automatically adapt their selection to a real-time update of the test taker’s ability estimate. This book covers such key features of adaptive testing as item selection and ability estimation, adaptive testing with multidimensional abilities, sequencing adaptive test batteries, multistage adaptive testing, item-pool design and maintenance, estimation of item and item-family parameters, item and person fit, as well as adaptive mastery and classification testing. It also shows how these features are used in the daily operations of several large-scale adaptive testing programs. Wim J. van der Linden is Chief Research Scientist at CTB/McGraw-Hill, Monterey, CA. His specialization is psychometric theory and methods, and he has been an active researcher of adaptive testing throughout his career. For Springer, he wrote Linear Models for Optimal Test Design (2005) and co-edited Handbook of Modern Item ResponseTheory (1997). He is a past president of the Psychometric Society and recipient of lifetime achievement awards from the National Council for Measurement in Education (NCME) and the Association of Test Publishers (ATP). Cees A. W. Glas is Professor of Social Science Research Methodology, University of Twente, the Netherlands. His specialization is psychometric theory and methods, with an emphasis on item response theory, adaptive testing, model fit analysis, and missing data. Professor Glas is a co-author of Educational Evaluation, Assessment, and Monitoring (Swets & Zetlinger, 2003). Currently, he is a member of the Editorial Board of Psychometrika and serves as a technical consultant to the OECD programs for international student assessment (PISA) and the assessment of adult competencies (PIAAC).
Computer adaptive testing. --- Educational tests and measurements. --- Graduate Management Admission Test. --- Psychometrics. --- Statistics. --- Computer adaptive testing --- Education --- Mathematics --- Theory & Practice of Education --- Mathematical Statistics --- Social Sciences --- Physical Sciences & Mathematics --- Achievement tests. --- Scholastic achievement tests --- School achievement tests --- Adaptive testing, Computer --- CAT (Computer adaptive testing) --- Computer adaptive tests --- Computerized adaptive testing --- Assessment. --- Statistics for Social Science, Behavorial Science, Education, Public Policy, and Law. --- Assessment, Testing and Evaluation. --- Statistics for Social Science, Behavioral Science, Education, Public Policy, and Law. --- Measurement, Mental --- Measurement, Psychological --- Psychological measurement --- Psychological scaling --- Psychological statistics --- Psychology --- Psychometry (Psychophysics) --- Scaling, Psychological --- Psychological tests --- Scaling (Social sciences) --- Statistical analysis --- Statistical data --- Statistical methods --- Statistical science --- Econometrics --- Measurement --- Scaling --- Methodology --- Academic achievement --- Educational tests and measurements --- Examinations --- Ability --- Competency-based educational tests --- Testing --- Educational tests and measuremen. --- Statistics for Social Sciences, Humanities, Law. --- Statistics .
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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.
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Cognitive science is an interdisciplinary field in the study of the mind and intelligence. The term cognition refers to a variety of mental processes, including perception, problem solving, learning, decision making, language use, and emotional experience. The basis of the cognitive sciences is the contribution of philosophy and computing to the study of cognition. Computing is very important in the study of cognition because computer-aided research helps to develop mental processes, and computers are used to test scientific hypotheses about mental organization and functioning. This book provides a platform for reviewing these disciplines and presenting cognitive research as a separate discipline.
Information technology industries --- computer adaptive testing --- code tracing --- basic programming skills --- internet of thing (IoT) --- eye tracking --- heart rate (HR) --- measurements --- data analysis --- Internet addiction --- dysfunctional emotions --- coping strategies --- emotional problems --- human-AI interaction --- interaction design --- Kansei engineering --- user satisfaction --- voice-based intelligent system --- dynamic gesture recognition --- gesture spotting --- self-organizing map --- computational psychology --- computational cognitive modeling --- machine learning --- concept blending --- conceptual combinations --- recall --- computational creativity --- cognition --- instance selection --- clustering --- information processing --- cognitive aspects --- remote --- virtual simulation --- incident commander --- user experiences --- problem solving --- decision making --- assessment --- learning --- privacy-preserving computations --- homomorphic encryption --- EEG signals --- school children --- functional vision --- vision screening --- vision training --- eye-tracking --- stakeholders --- human-robot interaction --- social gaze --- eye-to-eye contact --- emotional interfaces --- eye-brain-computer interfaces --- attention --- reflection --- usability --- brain hemispheric lateralization --- online educational material --- instructional design --- methodology --- model --- virtual reality --- virtual environment --- stress --- spaceflight --- training --- EEG --- emotion --- neural networks --- M3GP --- BED --- Emotiv --- multiclass --- deep learning --- traffic accident --- spatially prolonged risk --- Gestalt --- proximity --- open data --- computer adaptive testing --- code tracing --- basic programming skills --- internet of thing (IoT) --- eye tracking --- heart rate (HR) --- measurements --- data analysis --- Internet addiction --- dysfunctional emotions --- coping strategies --- emotional problems --- human-AI interaction --- interaction design --- Kansei engineering --- user satisfaction --- voice-based intelligent system --- dynamic gesture recognition --- gesture spotting --- self-organizing map --- computational psychology --- computational cognitive modeling --- machine learning --- concept blending --- conceptual combinations --- recall --- computational creativity --- cognition --- instance selection --- clustering --- information processing --- cognitive aspects --- remote --- virtual simulation --- incident commander --- user experiences --- problem solving --- decision making --- assessment --- learning --- privacy-preserving computations --- homomorphic encryption --- EEG signals --- school children --- functional vision --- vision screening --- vision training --- eye-tracking --- stakeholders --- human-robot interaction --- social gaze --- eye-to-eye contact --- emotional interfaces --- eye-brain-computer interfaces --- attention --- reflection --- usability --- brain hemispheric lateralization --- online educational material --- instructional design --- methodology --- model --- virtual reality --- virtual environment --- stress --- spaceflight --- training --- EEG --- emotion --- neural networks --- M3GP --- BED --- Emotiv --- multiclass --- deep learning --- traffic accident --- spatially prolonged risk --- Gestalt --- proximity --- open data
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Cognitive science is an interdisciplinary field in the study of the mind and intelligence. The term cognition refers to a variety of mental processes, including perception, problem solving, learning, decision making, language use, and emotional experience. The basis of the cognitive sciences is the contribution of philosophy and computing to the study of cognition. Computing is very important in the study of cognition because computer-aided research helps to develop mental processes, and computers are used to test scientific hypotheses about mental organization and functioning. This book provides a platform for reviewing these disciplines and presenting cognitive research as a separate discipline.
Information technology industries --- computer adaptive testing --- code tracing --- basic programming skills --- internet of thing (IoT) --- eye tracking --- heart rate (HR) --- measurements --- data analysis --- Internet addiction --- dysfunctional emotions --- coping strategies --- emotional problems --- human–AI interaction --- interaction design --- Kansei engineering --- user satisfaction --- voice-based intelligent system --- dynamic gesture recognition --- gesture spotting --- self-organizing map --- computational psychology --- computational cognitive modeling --- machine learning --- concept blending --- conceptual combinations --- recall --- computational creativity --- cognition --- instance selection --- clustering --- information processing --- cognitive aspects --- remote --- virtual simulation --- incident commander --- user experiences --- problem solving --- decision making --- assessment --- learning --- privacy-preserving computations --- homomorphic encryption --- EEG signals --- school children --- functional vision --- vision screening --- vision training --- eye-tracking --- stakeholders --- human–robot interaction --- social gaze --- eye-to-eye contact --- emotional interfaces --- eye–brain–computer interfaces --- attention --- reflection --- usability --- brain hemispheric lateralization --- online educational material --- instructional design --- methodology --- model --- virtual reality --- virtual environment --- stress --- spaceflight --- training --- EEG --- emotion --- neural networks --- M3GP --- BED --- Emotiv --- multiclass --- deep learning --- traffic accident --- spatially prolonged risk --- Gestalt --- proximity --- open data --- n/a --- human-AI interaction --- human-robot interaction --- eye-brain-computer interfaces
Choose an application
Cognitive science is an interdisciplinary field in the study of the mind and intelligence. The term cognition refers to a variety of mental processes, including perception, problem solving, learning, decision making, language use, and emotional experience. The basis of the cognitive sciences is the contribution of philosophy and computing to the study of cognition. Computing is very important in the study of cognition because computer-aided research helps to develop mental processes, and computers are used to test scientific hypotheses about mental organization and functioning. This book provides a platform for reviewing these disciplines and presenting cognitive research as a separate discipline.
computer adaptive testing --- code tracing --- basic programming skills --- internet of thing (IoT) --- eye tracking --- heart rate (HR) --- measurements --- data analysis --- Internet addiction --- dysfunctional emotions --- coping strategies --- emotional problems --- human–AI interaction --- interaction design --- Kansei engineering --- user satisfaction --- voice-based intelligent system --- dynamic gesture recognition --- gesture spotting --- self-organizing map --- computational psychology --- computational cognitive modeling --- machine learning --- concept blending --- conceptual combinations --- recall --- computational creativity --- cognition --- instance selection --- clustering --- information processing --- cognitive aspects --- remote --- virtual simulation --- incident commander --- user experiences --- problem solving --- decision making --- assessment --- learning --- privacy-preserving computations --- homomorphic encryption --- EEG signals --- school children --- functional vision --- vision screening --- vision training --- eye-tracking --- stakeholders --- human–robot interaction --- social gaze --- eye-to-eye contact --- emotional interfaces --- eye–brain–computer interfaces --- attention --- reflection --- usability --- brain hemispheric lateralization --- online educational material --- instructional design --- methodology --- model --- virtual reality --- virtual environment --- stress --- spaceflight --- training --- EEG --- emotion --- neural networks --- M3GP --- BED --- Emotiv --- multiclass --- deep learning --- traffic accident --- spatially prolonged risk --- Gestalt --- proximity --- open data --- n/a --- human-AI interaction --- human-robot interaction --- eye-brain-computer interfaces
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