Computational psychometrics : new methodologies for a new generation of digital learning and assessment : with examples in R and Python /
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Imprint: | Cham, Switzerland : Springer, 2021. |
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Description: | 1 online resource |
Language: | English |
Series: | Methodology of educational measurement and assessment, 2367-1718 Methodology of educational measurement and assessment, |
Subject: | |
Format: | E-Resource Book |
URL for this record: | http://pi.lib.uchicago.edu/1001/cat/bib/12874761 |
Table of Contents:
- 1. Introduction. Computational Psychometrics: Towards a Principled Integration of Data Science and Machine Learning Techniques into Psychometrics (Alina A. von Davier, Robert Mislevy and Jiangang Hao)
- Part I. Conceptualization. 2. Next generation learning and assessment: what, why and how (Robert Mislevy)
- 3. Computational psychometrics (Alina A. von Davier, Kristen DiCerbo and Josine Verhagen)
- 4. Virtual performance-based assessments (Jessica Andrews-Todd, Robert Mislevy, Michelle LaMar and Sebastiaan de Klerk)
- 5. Knowledge Inference Models Used in Adaptive Learning (Maria Ofelia Z. San Pedro and Ryan S. Baker)
- Part II. Methodology. 6. Concepts and models from Psychometrics (Robert Mislevy and Maria Bolsinova)
- 7. Bayesian Inference in Large-Scale Computational Psychometrics (Gunter Maris, Timo Bechger and Maarten Marsman)
- 8. Data science perspectives (Jiangang Hao and Robert Mislevy)
- 9. Supervised machine learning (Jiangang Hao)
- 10. Unsupervised machine learning (Pak Chunk Wong)
- 11. AI and deep learning for educational research (Yuchi Huang and Saad M. Khan)
- 12. Time series and stochastic processes (Peter Halpin, Lu Ou and Michelle LaMar)
- 13. Social network analysis (Mengxiao Zhu)
- 14. Text mining and automated scoring (Michael Flor and Jiangang Hao).