Applications of Bayesian decision theory to sequential mastery testing

Hans J. Vos

Research output: Contribution to journalArticleAcademicpeer-review

10 Citations (Scopus)

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.
Original languageEnglish
Pages (from-to)271-292
Number of pages22
JournalJournal of educational and behavioral statistics
Volume24
Issue number3
DOIs
Publication statusPublished - 1999

Keywords

  • Bayesian decision theory
  • Dynamic programming
  • Beta-binomial model
  • Monotonicity conditions
  • Sequential mastery testing

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