Variations on Stochastic Curtailment in Sequential Mastery Testing

In sequential mastery testing (SMT), assessment via computer is used to classify examinees into one of two mutually exclusive categories. Unlike paper-and-pencil tests, SMT has the capability to use variable-length stopping rules. One approach to shortening variable-length tests is stochastic curtailment, which halts examination if the probability of changing classification decisions is low. The estimation of such a probability is therefore a critical component of a stochastically curtailed test. This article examines several variations on stochastic curtailment where the key probability is estimated more aggressively than the standard formulation, resulting in additional savings in average test length (ATL). In two simulation sets, the variations successfully reduced the ATL, and in many cases the average loss, compared with the standard formulation.