Abstract
Bayesian network models can not only provide belief estimates for proficiencies and claims about the learner, but they can also be used to explain the basis of those estimates. The quantity defined as the weight of evidence associated with a task is useful for explanation, as well as debugging models (explaining unexpected results). Expected weight of evidence is useful for assembling assessments either adaptively or in fixed forms.
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Notes
- 1.
On a color screen, this rendering uses pale blue for positive evidence, and red for negative evidence.
- 2.
This is sometimes called a confusion matrix , referring to its off-diagonal elements.
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© 2015 Springer Science+Business Media New York
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Almond, R., Mislevy, R., Steinberg, L., Yan, D., Williamson, D. (2015). Explanation and Test Construction. In: Bayesian Networks in Educational Assessment. Statistics for Social and Behavioral Sciences. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-2125-6_7
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DOI: https://doi.org/10.1007/978-1-4939-2125-6_7
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