Diagnostic classification models (DCMs) can provide multidimensional diagnostic feedback about students' mastery levels of knowledge components or attributes. One advantage of using DCMs is the ability to accurately and reliably classify students into mastery levels with a relatively small number of items per attribute. Combining DCMs with…

a-Stratified computerized adaptive testing with b-blocking (AST), as an alternative to the widely used maximum Fisher information (MFI) item selection method, can effectively balance item pool usage while providing accurate latent trait estimates in computerized adaptive testing (CAT). However, previous comparisons of these methods have treated…

The intent of this research was to find an item selection procedure in the multidimensional computer adaptive testing (CAT) framework that yielded higher precision for both the domain and composite abilities, had a higher usage of the item pool, and controlled the exposure rate. Five multidimensional CAT item selection procedures (minimum angle;…

This study compares the four existing procedures handling the item selection in severely constrained computerized adaptive tests (CAT). These procedures include weighted deviation model (WDM), weighted penalty model (WPM), maximum priority index (MPI), and shadow test approach (STA). Severely constrained CAT refer to those adaptive tests seeking…

This study compares the progressive-restricted standard error (PR-SE) exposure control procedure to three commonly used procedures in computerized adaptive testing, the randomesque, Sympson-Hetter (SH), and no exposure control methods. The performance of these four procedures is evaluated using the three-parameter logistic model under the…

The Monte Carlo approach which has previously been implemented in traditional computerized adaptive testing (CAT) is applied here to cognitive diagnostic CAT to test the ability of this approach to address multiple content constraints. The performance of the Monte Carlo approach is compared with the performance of the modified maximum global…

In the human sciences, a common assumption is that latent traits have a hierarchical structure. Higher order item response theory models have been developed to account for this hierarchy. In this study, computerized adaptive testing (CAT) algorithms based on these kinds of models were implemented, and their performance under a variety of…

Successful administration of computerized adaptive testing (CAT) programs in educational settings requires that test security and item exposure control issues be taken seriously. Developing an item selection algorithm that strikes the right balance between test precision and level of item pool utilization is the key to successful implementation…

The methods of alpha-stratified adaptive testing and constrained adaptive testing with shadow tests are combined in this study. The advantages are twofold. First, application of the shadow test allows the researcher to implement any type of constraint on item selection in alpha-stratified adaptive testing. Second, the result yields a simple set of…

The purpose of this study was to compare a proposed revised a-stratified, or alpha-stratified, USTR method of test item selection with the original alpha-stratified multistage computerized adaptive testing approach (STR) and the use of maximum Fisher information (FSH) with respect to test efficiency and item pool usage using simulated computerized…

Proposes a new multistage adaptive-testing procedure that factors the discrimination parameter (alpha) into the item-selection process. Simulation studies indicate that the new strategy results in tests that are well-balanced, with respect to item exposure, and efficient. (SLD)

Studied the impact of using five different exposure control algorithms in two sizes of item pool calibrated using the generalized partial credit model. Simulation results show that the a-stratified design, in comparison to a no-exposure control condition, could be used to reduce item exposure and overlap and increase pool use, while degrading…

Demonstrates mathematically that if every item in an item pool has an equal possibility to be selected from the pool in a fixed-length computerized adaptive test, the number of overlapping items among an alpha randomly sampled examinees follows the hypergeometric distribution family for alpha greater than or equal to 1. (SLD)

Provides an overview of computerized adaptive testing (CAT) and introduces contributions to this special issue. CAT elements discussed include item selection, estimation of the latent trait, item exposure, measurement precision, and item-bank development. (SLD)

A previously developed method of automatically selecting items for inclusion in a test subject to constraints on item content and statistical properties is applied to real data. Two tests are first assembled by experts in test construction who normally assemble such tests on a routine basis. Using the same pool of items and constraints articulated…