Uncertainty in Artificial Intelligence
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Bayes Nets in Educational Assessment: Where Do the Numbers Come From?
Robert Mislevy, Russell Almond, Duanli Yan, Linda Steinberg
Abstract:
As observations and student models become complex, educational assessments that exploit advances in technology and cognitive psychology can outstrip familiar testing models and analytic methods. Within the Portal conceptual framework for assessment design, Bayesian inference networks (BINs) record beliefs about students' knowledge and skills, in light of what they say and do. Joining evidence model BIN fragments---which contain observable variables and pointers to student model variables---to the student model allows one to update belief about knowledge and skills as observations arrive. Markov Chain Monte Carlo (MCMC) techniques can estimate the required conditional probabilities from empirical data, supplemented by expert judgment or substantive theory. Details for the special cases of item response theory (IRT) and multivariate latent class modeling are given, with a numerical example of the latter.
Keywords: Item response theory, Markov Chain Monte Carlo, learning from data
Pages: 437-446
PS Link:
PDF Link: /papers/99/p437-mislevy.pdf
BibTex:
@INPROCEEDINGS{Mislevy99,
AUTHOR = "Robert Mislevy and Russell Almond and Duanli Yan and Linda Steinberg",
TITLE = "Bayes Nets in Educational Assessment: Where Do the Numbers Come From?",
BOOKTITLE = "Proceedings of the Fifteenth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-99)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "1999",
PAGES = "437--446"
}


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