Applications of Bayesian Decision Theory to Sequential Mastery Testing

Hans J. Vos

Journal of Educational and Behavioral Statistics, 1999, vol. 24, issue 3, 271-292

Abstract: The purpose of this paper is to formulate optimal sequential rules for mastery tests. The framework for the approach is derived from Bayesian sequential decision theory. Both a threshold and linear loss structure are considered. The binomial probability distribution is adopted as the psychometric model involved. Conditions sufficient for sequentially setting optimal cutting scores are presented. Optimal sequential rules will be derived for the case of a subjective beta distribution representing prior true level of functioning. An empirical example of sequential mastery esting for concept-learning in medicine concludes the paper.

Keywords: Bayesian decision theory; beta-binomial model; dynamic programming; monotonicity conditions; sequential mastery testing (search for similar items in EconPapers)
Date: 1999
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Persistent link: https://EconPapers.repec.org/RePEc:sae:jedbes:v:24:y:1999:i:3:p:271-292

DOI: 10.3102/10769986024003271

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