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R. G. Almond, R. J. Mislevy L. S. Steinberg D. Yan & D. M. Williamson (2015). Bayesian Networks in Educational Assessment. NY: Springer. ISBN: 1493921258. DOI: 10.1007/978-0-387-98138-3
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R. G. Almond, R. J. Mislevy L. S. Steinberg D. Yan & D. M. Williamson (2015). Bayesian Networks in Educational Assessment. NY: Springer. ISBN: 1493921258. DOI: 10.1007/978-0-387-98138-3
Published online by Cambridge University Press:
01 January 2025
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References
Aguilera, P. A.Fernández, A.Fernández, R.Rumí, R. & Salmerón, A. (2011). Bayesian networks in environmental modelling. Environmental Modelling & Software26, 1376–1388CrossRefGoogle Scholar
De Klerk, S.Veldkamp, B. P.Eggen, TJHM (2015). Psychometric analysis of the performance data of simulation-based assessment: A systematic review and a Bayesian network example. Computers & Education85, 23–34CrossRefGoogle Scholar
Lucas, P. (2001). Bayesian networks in medicine: A model-based approach to medical decision making. In Proceedings of the EUNITE Workshop on Intelligent Systems in Patient Care, Vienna, pp. 73–97.Google Scholar
Mislevy, R. J., Almond, R. G., & Lukas, J. (2004). A brief introduction to evidence-centered design. CSE Technical Report. Los Angeles: The National Center for Research on Evaluation, Standards, and Student Testing (CRESST). http://www.cse.ucla.edu/products/reports/r632.pdf.Google Scholar
Mislevy, R. J., Almond, R. G., Yan, D., & Steinberg, L. S. (2000). Bayes nets in educational assessment: Where do the numbers come from? CSE Technical Report, 518.CrossRefGoogle Scholar
Neapolitan, R. E. (2003). Learning Bayesian networksNew York, NY: Prentice-HallGoogle Scholar
Pearl, J. (1988). Probabilistic reasoning in intelligent systems: Networks of plausible inferenceSan Francisco, CA: Morgan KaufmannGoogle Scholar
Schneiderman, H. (2004). Learning a restricted Bayesian network for object detection. In Computer Vision and Pattern Recognition, 2004. CVPR 2004. Proceedings of the 2004 IEEE Computer Society Conference on IEEE (Vol. 2, pp. II–II).CrossRefGoogle Scholar
Tatsuoka, K. K. (1983). Rule-space: An approach for dealing with misconceptions based on item response theory. Journal of Educational Measurement20, 34–38CrossRefGoogle Scholar