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.