Computational Psychometrics: New Methodologies for a New Generation of Digital Learning and Assessment With Examples in R and Python /
| Collectivité auteur: | |
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| Autres auteurs: | , , |
| Résumé: | X, 262 p. 1 illus. text |
| Langue: | anglais |
| Publié: |
Cham :
Springer International Publishing : Imprint: Springer,
2021.
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| Édition: | 1st ed. 2021. |
| Collection: | Methodology of Educational Measurement and Assessment,
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| Sujets: | |
| Accès en ligne: | https://doi.org/10.1007/978-3-030-74394-9 |
| Format: | Électronique Livre |
Table des matières:
- 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).