Applications of Bayesian Decision Theory to Sequential Mastery Testing
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 mo...
| Veröffentlicht in: | Journal of Educational and Behavioral Statistics. - SAGE Publishing, 1976. - 24(1999), 3, Seite 271-292 |
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| Format: | Online-Aufsatz |
| Sprache: | English |
| Veröffentlicht: |
1999
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| Zugriff auf das übergeordnete Werk: | Journal of Educational and Behavioral Statistics |
| Schlagworte: | Bayesian decision theory Beta-binomial model Dynamic programming Monotonicity conditions Sequential mastery testing Behavioral sciences Education Mathematics Health sciences |
| Zusammenfassung: | 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 testing for concept-learning in medicine concludes the paper. |
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| ISSN: | 19351054 |