Research for Practical Issues and Solutions in Computerized Multistage 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.

• 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