Computational Thinking (CT) skill was a part of the global framework of reference on Digital Literacy for Indicator 4.4.2, widely developed in mathematics and science learning. This study aimed to promote an assessment tool using a two-tier Computerized Adaptive Test (CAT). The study used the Design and Development Research (DDR) method with four…

David Thissen, a professor in the Department of Psychology and Neuroscience, Quantitative Program at the University of North Carolina, has consulted and served on technical advisory committees for assessment programs that use item response theory (IRT) over the past couple decades. He has come to the conclusion that there are usually two purposes…

Most computerized adaptive tests (CATs) have been studied using the framework of unidimensional item response theory. However, many psychological variables are multidimensional and might benefit from using a multidimensional approach to CATs. This study investigated the accuracy, fidelity, and efficiency of a fully multidimensional CAT algorithm…

Item calibration is an essential issue in modern item response theory based psychological or educational testing. Due to the popularity of computerized adaptive testing, methods to efficiently calibrate new items have become more important than that in the time when paper and pencil test administration is the norm. There are many calibration…

Imposed time limits on computer adaptive tests (CATs) can result in examinees having difficulty completing all items, thus compromising the validity and reliability of ability estimates. In this study, the effects of speededness were explored in a simulated CAT environment by varying examinee response patterns to end-of-test items. Expectedly,…

R. J. Owen (1975) proposed an approximate empirical Bayes procedure for item selection in adaptive testing. The procedure replaces the true posterior by a normal approximation with closed-form expressions for its first two moments. This approximation was necessary to minimize the computational complexity involved in a fully Bayesian approach, but…

Item parameter estimates vary for a variety of reasons, including estimation error, characteristics of the examinee samples, and context effects (e.g., item location effects, section location effects, etc.). Although we expect variation based on theory, there is reason to believe that observed variation in item parameter estimates exceeds what…