The nature and use of state mastery models

Abstract

This paper provides a review of a class of probabilistic models that has been developed for use in the assessment of trait or competency acquisition. Consideration is given to the relative merits and limitations of this class of state models, under which trait acquisition is conceived as being "all-ornone," as compared with those occurring under an alternative conceptual framework, in which trait acquisition is assumed to be gradual. In addition, some of the applications of these state models are presented, including the establishment of mastery classification decisions and the assessment of consistency with respect to items and classification. Finally, some extensions to the class of state models, which may be helpful in increasing the applicability of this class of models, are presented.