| ISBN13: | 9780367207816 |
| ISBN10: | 0367207818 |
| Kötéstípus: | Puhakötés |
| Terjedelem: | 516 oldal |
| Méret: | 234x156 mm |
| Nyelv: | angol |
| Illusztrációk: | 140 Illustrations, black & white; 140 Line drawings, black & white; 55 Tables, black & white |
| 700 |
Valószínűségelmélet és matematikai statisztika
Kiegészítő eszközök
További könyvek a számítástechnika területén
Elméleti pszichológia
Felsőoktatás, felnőttképzés
További könyvek a pedagógia területén
Valószínűségelmélet és matematikai statisztika (karitatív célú kampány)
Kiegészítő eszközök (karitatív célú kampány)
További könyvek a számítástechnika területén (karitatív célú kampány)
Elméleti pszichológia (karitatív célú kampány)
Felsőoktatás, felnőttképzés (karitatív célú kampány)
További könyvek a pedagógia területén (karitatív célú kampány)
Research for Practical Issues and Solutions in Computerized Multistage Testing
GBP 46.99
Kattintson ide a feliratkozáshoz
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
