A version of sequential mastery testing is studied in which response behavior is modeled by an item response theory (IRT) model. First, a general theoretical framework is sketched that is based on a combination of Bayesian sequential decision theory and item response theory. A discussion follows on how IRT based sequential mastery testing can be…

An adaptive test can usually match or exceed the measurement precision of conventional tests several times its length. This increased efficiency is not without costs, however, as the models underlying adaptive testing make strong assumptions about examinees and items. Most troublesome is the assumption that item pools are unidimensional. Truly…

Many applications of educational testing have a missing data aspect (MDA). This MDA is perhaps most pronounced in item banking, where each examinee responds to a different subtest of items from a large item pool and where both person and item parameter estimates are needed. The Rasch model is emphasized, and its non-parametric counterpart (the…

This brief overview notes that an adaptive test differs from standardized achievement tests in that it does not consist of a certain set of items that are administered to a group of examinees. Instead, the test is individualized for each examinee. The items administered to the examinee are selected from a large pool of items on the basis of the…

The feasibility of implementing self-paced computerized tailored testing evaluation methods in an undergraduate measurement and evaluation course, and possible differences in achievement levels under a paced versus self-paced testing schedule were investigated. A maximum likelihood tailored testing procedure based on the simple logistic model had…

Describes a system in which questions tailored to the examinee's capabilities are administered by computer. Enumerates possible benefits of the system, reviews the "state of the art," and predicts potential applications of computerized adaptive testing. (MCG)

The purposes of this research study were to develop and field test anchor-based judgmental methods for enabling test specialists to estimate item difficulty statistics. The study consisted of three related field tests. In each, researchers worked with six Law School Admission Test (LSAT) test specialists and one or more of the LSAT subtests. The…

Computer-based assessment appears to offer the promise of radically improving both how assessments are implemented and the quality of the information they can deliver. However, as many states consider whether to embrace this new technology, serious concerns remain about the fairness of the new systems and the readiness of states (and districts and…

In constrained adaptive testing, the numbers of constraints needed to control the content of the tests can easily run into the hundreds. Proper initialization of the algorithm becomes a requirement because the presence of large numbers of constraints slows down the convergence of the ability estimator. In this paper, an empirical initialization of…

The case of adaptive testing under a multidimensional logistic response model is addressed. An adaptive algorithm is proposed that minimizes the (asymptotic) variance of the maximum-likelihood (ML) estimator of a linear combination of abilities of interest. The item selection criterion is a simple expression in closed form. In addition, it is…

This paper describes an approach to large-scale assessment that uses tests that are delivered to students over the Internet and that are tailored (adapted) to each student's own level of proficiency. A brief background on large-scale assessment is followed by a description of this new technology and an example. Issues that need to be investigated…

The multistage alpha-stratified computerized adaptive testing (CAT) design advocated a new philosophy of pool management and item selection using low discriminating items first. It has been demonstrated through simulation studies to be effective both in reducing item overlap rate and enhancing pool utilization with certain pool types. Based on…

This paper proposes an item selection algorithm that can be used to neutralize the effect of time limits in computer adaptive testing. The method is based on a statistical model for the response-time distributions of the test takers on the items in the pool that is updated each time a new item has been administered. Predictions from the model are…

Research discussed in this paper was conducted as part of an ongoing large-scale simulation study to evaluate methods of calibrating pretest items for computerized adaptive testing (CAT) pools. The simulation was designed to mimic the operational CAT Armed Services Vocational Aptitude Battery (ASVAB) testing program, in which a single pretest item…

Item scores that do not fit an assumed item response theory model may cause the latent trait value to be estimated inaccurately. For computerized adaptive tests (CAT) with dichotomous items, several person-fit statistics for detecting nonfitting item score patterns have been proposed. Both for paper-and-pencil (P&P) test and CATs, detection of…