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E-grāmata: Research for Practical Issues and Solutions in Computerized Multistage Testing

Edited by , Edited by , Edited by (Educational Testing Service, Princeton, New Jersey, USA)
  • Formāts: 516 pages
  • Izdošanas datums: 27-Dec-2024
  • Izdevniecība: Routledge
  • Valoda: eng
  • ISBN-13: 9780429558535
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This volume presents a comprehensive collection of the latest research findings supporting the current and future implementations and applications of computerized multistage testing (MST).



This volume presents a comprehensive collection of the latest research findings supporting the current and future implementations and applications of computerized multistage testing (MST).

As a sequel to the widely acclaimed Computerized Multistage Testing: Theory and Applications (2014) by Yan, von Davier, and Lewis, this volume delves into the experiences, considerations, challenges, and lessons learned over the past years. It also offers practical approaches and solutions to the issues encountered. The topics covered include purposeful MST designs, practical approaches for optimal design, assembly strategies for accuracy and efficiency, hybrid designs, MST with natural language processing, practical routing considerations and methodologies, item calibration and proficiency estimation methods, routing and classification accuracy, added value of process data, prediction and evaluation of MST performance, cognitive diagnostic MST, differential item functioning, robustness of statistical methods, simulations, test security, the new digital large-scale Scholastic Aptitude Test, software for practical assessment and simulations, artificial intelligence impact, and the future of adaptive MST.

This volume is intended for students, faculty, researchers, practitioners, and education officers in the fields of educational measurement and evaluation in the United States and internationally.

1. Introduction - History of Computerized Adaptive and Multistage Testing Part I: MST Design and Assembly
2. Purposeful Design for Useful MST: Considerations of choice in MST
3. MST Strategic Design Issues and Implementation
4. Designing Multistage Tests to Meet Accuracy and Efficiency Goals
5. Hybrid MST Designs in Passage-based Adaptive Tests
6. A Practical Approach to Find Optimal Design of Multistage Tests
7. MST and natural language processing for learning sciences Part II: MST Routing, Scoring, and Estimation
8. Multistage Testing with Inter-Sectional Routing for Short-Length Tests
9. Effect of Routing Errors on the Psychometric Properties of Multistage Tests
10. Item Calibration in MST
11. IRT Proficiency Estimation Methods Under Adaptive Multistage Testing
12. Multistage Tests Under D-Scoring Approach
13. Development and Application of Probability-weighted Classification for Multistage Testing
14. Creating Value from Process Data: Implications for Multistage Testing Part III: MST Evaluations
15. Predicting and Evaluating the Performance of Multistage Tests
16. Cognitive Diagnostic Multistage Adaptive Test
17. DIF in Multistage Testing
18. Robustness of current statistical methods to handle multistage test data
19. Conducting Simulation Studies in Computerized MST Research
20. Test Security Considerations for MST Part IV: Applications and Technologies
21. The new SAT - Considerations for the new SAT design and implementation
22. Build High-quality MST Panels with R package Rmst/xxIRT
23. Bayesian Inference for MST with R package dexterMST
24. Overview of Simulation Software
25. How Will AI Change Adaptive Testing?
26. Afterword - The Emergence of Personalized Ensemble Testing

Duanli Yan is a Director of Computational Research at Educational Testing Services, Princeton, New Jersey, USA. She is also an adjunct professor at Rutgers University and Fordham University and has extensive experience in innovative psychometric research and development. She has published many books and received many awards, including the 2016 AERA D Significant Contribution to Educational Measurement and Research Methodology Award, and the 2022 and 2023 NCME Bradley Hanson Award.

Alina A. von Davier is the Chief of Assessment at Duolingo, Pittsburgh, Pennsylvania, USA. She leads the Duolingo English Test research and development area. She is a researcher in computational psychometrics, machine learning, and education. Von Davier is an innovator and an executive leader with over 20 years of experience in EdTech and in the assessment industry. In 2022, she joined the University of Oxford as an Honorary Research Fellow, and Carnegie Mellon University as a Senior Research Fellow.

David J. Weiss is a Professor of Psychology at University of Minnesota, Minnesota, USA. He has been continuously active in computerized adaptive testing (CAT) research since 1970, including hosting six international CAT conferences. He co-founded the International Association for Computerized Adaptive Testing, the Assessment Systems Corporation, and the Insurance Testing Corporation and was the founding editor of Applied Psychological Measurement and the Journal of Computerized Adaptive Testing.
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