Research for Practical Issues and Solutions in Computerized Multistage Testing (eBook, ePUB)

Redaktion: Yan, Duanli; Weiss, David J.; Davier, Alina A. Von

• Format: ePub

Produktbeschreibung

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.

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Produktdetails

• Verlag: Taylor & Francis eBooks
• Seitenzahl: 516
• Erscheinungstermin: 27. Dezember 2024
• Englisch
• ISBN-13: 9780429558535
• Artikelnr.: 72481098

Autorenporträt

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.

Inhaltsangabe

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