Computerized classification testing (CCT) is a subset of computerized adaptive testing (CAT), and it aims to classify examinees into one of at least two possible categories that denote results such as pass/fail or non-mastery/partial mastery/mastery. Therefore, CCTs focus on increasing the accuracy of classification which is different from CATs designed for precise measurement. The termination rule is one of the key components of CCT. However, as pointed out by Nydick (2013), most CCTs (i.e., UCCTs) were designed under unidimensional item response theory (IRT), in which the unidimensionality assumption is easily violated in practice. Thus, researchers then began to construct multidimensional CCT termination rules (i.e., MCCT) based on multidimensional IRT. To date, however, these rules still have some deficiencies in terms of classification accuracy or test efficiency.
Most current studies on termination rules of MCCT are based on termination rules of UCCT. In UCCTs, termination rules require setting a cut point, ${{\theta }_{0}}$, of the latent trait to calculate the statistics; and when they are extended from UCCT to MCCT, the cut point will become a classification bound curve or even a surface (i.e., $g(\theta )=0$). At this time, a question is how to convert the curve or surface into ${{\theta }_{0}}$. To this end, the projected sequential probability ratio test (P-SPRT), constrained SPRT (C-SPRT; Nydick, 2013), and multidimensional generalized likelihood ratio (M-GLR) were respectively proposed to solve the problem in different ways. Among them, P-SPRT and C-SPRT choose specific points on g(θ) as the approximate cut point, ${{\hat{\theta }}_{0}}$, by projecting into Euclidean space or constraining on g(θ) respectively; as for M-GLR, because the generalized likelihood ratio statistic can be calculated without a cut point, it can be directly employed in MCCT. To overcome the limitation that P-SPRT may lead to unstable results at the beginning of the test, this study proposed the Mahalanobis distance-based SPRT (Mahalanobis-SPRT).
In addition, stochastic curtailment is a technique for shortening the test length by predicting whether the classification of participants will change as the test continues. This article also combined M-GLR with the stochastic curtailment and proposed M-GLR with stochastic curtailment (M-SCGLR).
A full-scale simulation study was conducted to (1) compare both the Mahalanobis-SPRT and M-SCGLR with the P-SPRT, C-SPRT, M-GLR, and multidimensional stochastically curtailed SPRT (M-SCSPRT) under varying conditions; (2) compare the classification performance of the above six termination rules for participants with specific abilities to explore whether there is a significant difference in the sensitivity of various rules to classify specific participants. To achieve the first research objective, three levels of correlation between dimensions (ρ=0, 0.5, and 0.8), two item bank structures (within-item multidimensionality and between-item multidimensionality), and two kinds of classification boundary (compensatory boundary and non-compensatory boundary) were considered; to achieve the second objective, 36 specific ability points $({{\theta }_{1}},{{\theta }_{2}})$ were generated where ${{\theta }_{1}},{{\theta }_{2}}\in \{-0.5,-0.3,-0.1,0.1,0.3,0.5\}$. The results showed that: (1) when the compensatory classification function was used, the Mahalanobis-SPRT led to higher classification accuracy and similar test length to the rules without stochastic curtailment; (2) under almost all conditions, the M-SCGLR not only possessed higher precision but also maintained the short test length, compared to M-SCSPRT that also uses stochastic curtailment; (3) the six termination rules showed a consistent change in the sensitivity of the precision and test length to specific participants.
To sum up, two new MCCT termination rules (Mahalanobis-SPRT and M-SCGLR) are put forward in this article. Although the simulation results are very promising, several research directions merit further investigation, such as the development of MCCT termination rules for more than two categories, and the construction of MCCT termination rules by incorporating process data like the response time.
