The emergence of sophisticated AI tools such as ChatGPT, coupled with the transition to remote delivery of educational assessments in the COVID-19 era, has led to increasing concerns about academic integrity and test security. Using AI tools, test takers can produce high-quality texts effortlessly and use them to game assessments. It is thus…

Allowance for multiple chances to answer constructed response questions is a prevalent feature in computer-based homework and exams. We consider the use of item response theory in the estimation of item characteristics and student ability when multiple attempts are allowed but no explicit penalty is deducted for extra tries. This is common…

This research derived information functions and proposed new scalar information indices to examine the quality of multidimensional forced choice (MFC) items based on the RANK model. We also explored how GGUM-RANK information, latent trait recovery, and reliability varied across three MFC formats: pairs (two response alternatives), triplets (three…

Cognitive diagnosis models provide profile information about a set of latent binary attributes, whereas item response models yield a summary report on a latent continuous trait. To utilize the advantages of both models, higher order cognitive diagnosis models were developed in which information about both latent binary attributes and latent…

Computer-based tests (CBTs) often use random ordering of items in order to minimize item exposure and reduce the potential for answer copying. Little research has been done, however, to examine item position effects for these tests. In this study, different versions of a Rasch model and different response time models were examined and applied to…

In computerized adaptive testing (CAT), ensuring the security of test items is a crucial practical consideration. A common approach to reducing item theft is to define maximum item exposure rates, i.e., to limit the proportion of examinees to whom a given item can be administered. Numerous methods for controlling exposure rates have been proposed…

Examined the effects of model misspecification on the precision of decisions made using the sequential probability ratio test (SPRT) in computer testing. Simulation results show that the one-parameter logistic model produced more errors than the true model. (SLD)

Describes an integrated approach to test development and administration called computer-adaptive sequential testing (CAST). CAST incorporates adaptive testing methods with automated test assembly. Describes the CAST framework and demonstrates several applications using a medical-licensure example. (SLD)

This paper defines Bayesian network models and examines their applications to IRT-based cognitive diagnostic modeling. These models are especially suited to building inference engines designed to be synchronous with the finer grained student models that arise in skills diagnostic assessment. Aspects of the theory and use of Bayesian network models…

Contrasts "mental models" used by automated scoring for the simulation division of the computerized Architect Registration Examination with those used by experienced human graders for 3,613 candidate solutions. Discusses differences in the models used and the potential of automated scoring to enhance the validity evidence of scores. (SLD)

Reviews research literature on comparability issues in computerized adaptive testing (CAT) and synthesizes issues specific to comparability and test security. Develops a framework for evaluating comparability that contains three categories of criteria: (1) validity; (2) psychometric property/reliability; and (3) statistical assumption/test…