Computer adaptive testing (CAT) is an increasingly common mode of test administration offering improved test security, better measurement precision, and the potential for shorter testing experiences. This article presents a new item selection algorithm based on a generalized objective function to support multiple types of testing conditions and…

In computerized adaptive testing, overexposure of items in the bank is a serious problem and might result in item compromise. We develop an item selection algorithm that utilizes the entire bank well and reduces the overexposure of items. The algorithm is based on collaborative filtering and selects an item in two stages. In the first stage, a set…

To improve the calibration accuracy and calibration efficiency of cognitive diagnostic computerized adaptive testing (CD-CAT) for new items and, ultimately, contribute to the widespread application of CD-CAT in practice, the current article proposed a Gini-based online calibration method that can simultaneously calibrate the Q-matrix and item…

Administration of patient-reported outcome measures (PROs), using multidimensional computer adaptive tests (MCATs) has the potential to reduce patient burden, but the efficiency of MCAT depends on the degree to which an individual's responses fit the psychometric properties of the assessment. Assessing patients' symptom burden through the…

In low-stakes assessment settings, students' performance is not only influenced by students' ability level but also their test-taking engagement. In computerized adaptive tests (CATs), disengaged responses (e.g., rapid guesses) that fail to reflect students' true ability levels may lead to the selection of less informative items and thereby…

Item preknowledge refers to the phenomenon in which some examinees have access to live items before taking a test. It is one of the most common and significant concerns within the testing industry. Thus, various statistical methods have been proposed to detect item preknowledge in computerized linear or adaptive testing. However, the success of…

For efficiency, Computerized Adaptive Test (CAT) algorithm selects items with the maximum information, typically with a 50% probability of being answered correctly. However, examinees may not be satisfied if they only correctly answer 50% of the items. Researchers discovered that changing the item selection algorithms to choose easier items (i.e.,…

The interest in the application of large-scale adaptive testing for secure tests has served to focus attention on issues that arise when theoretical advances are made operational. Many such issues in the application of large-scale adaptive testing for secure tests have more to do with changes in testing conditions than with testing paradigms. One…

A mathematical programming approach is presented for computer adaptive testing (CAT) with many constraints on the item and test attributes. Because mathematical programming problems have to be solved while the examinee waits for the next item, a fast implementation of the Branch-and-Bound algorithm is needed for this approach. Eight modifications…

Evaluates a method for item selection in adaptive testing that is based on Kullback-Leibler information (KLI) (T. Cover and J. Thomas, 1991). Simulation study results show that testing algorithms using KLI-based item selection perform better than or as well as those using Fisher information item selection. (SLD)

New items are being calibrated for the next generation of the computerized adaptive (CAT) version of the Armed Services Vocational Aptitude Battery (ASVAB) (Forms 5 and 6). The requirements that the items be "good" three-parameter logistic (3-PL) model items and typically "like" items in the previous CAT-ASVAB tests have…

This study examined the effectiveness of the Sympson and Hetter conditional procedure (SHC), a modification of the Sympson and Hetter (1985) algorithm, in controlling the exposure rates of items in a computerized adaptive testing (CAT) environment. The properties of the procedure were compared with those of the Davey and Parshall (1995) and the…

In the process of item calibration for a computerized adaptive test (CAT), many well-established calibrating packages show weakness in the estimation of item parameters. This paper introduces an on-line calibration algorithm based on the convexity of likelihood functions. This package consists of: (1) an algorithm that estimates examinee ability…

Many arguments have been made against allowing examinees to review and change their answers after completing a computer adaptive test (CAT). These arguments include: (1) increased bias; (2) decreased precision; and (3) susceptibility of test-taking strategies. Results of simulations suggest that the strength of these arguments is reduced or…

Two item selection algorithms, one based on maximal differentiation between examinees and one based on item response theory and maximum information for each examinee, were compared in simulated linear and adaptive tests of cognitive ability. Adaptive tests based on maximum information were clearly superior. (SLD)