A tree model for diagnostic educational testing is described along with Monte Carlo simulations designed to evaluate measurement accuracy based on the model. The model is implemented in an assessment of inferential reading comprehension, the Multiple-Choice Online Causal Comprehension Assessment (MOCCA), through a sequential, multidimensional,…

International large-scale assessments (ILSAs) transitioned from paper-based assessments to computer-based assessments (CBAs) facilitating the use of new item types and more effective data collection tools. This allows implementation of more complex test designs and to collect process and response time (RT) data. These new data types can be used to…

A family of learning models that integrates a cognitive diagnostic model and a higher-order, hidden Markov model in one framework is proposed. This new framework includes covariates to model skill transition in the learning environment. A Bayesian formulation is adopted to estimate parameters from a learning model. The developed methods are…

Many standardized tests are now administered via computer rather than paper-and-pencil format. In a computer-based testing environment, it is possible to record not only the test taker's response to each question (item) but also the amount of time spent by the test taker in considering and answering each item. Response times (RTs) provide…

The sequential probability ratio test (SPRT) is a common method for terminating item response theory (IRT)-based adaptive classification tests. To decide whether a classification test should stop, the SPRT compares a simple log-likelihood ratio, based on the classification bound separating two categories, to prespecified critical values. As has…

The item response times (RTs) collected from computerized testing represent an underutilized type of information about items and examinees. In addition to knowing the examinees' responses to each item, we can investigate the amount of time examinees spend on each item. Current models for RTs mainly focus on parametric models, which have the…

In this essay, the author tries to look forward into the 21st century to divine three things: (i) What skills will researchers in the future need to solve the most pressing problems? (ii) What are some of the most likely candidates to be those problems? and (iii) What are some current areas of research that seem mined out and should not distract…

A bivariate lognormal model for the distribution of the response times on a test by a pair of test takers is presented. As the model has parameters for the item effects on the response times, its correlation parameter automatically corrects for the spuriousness in the observed correlation between the response times of different test takers because…

During the early stage of computerized adaptive testing (CAT), item selection criteria based on Fisher"s information often produce less stable latent trait estimates than the Kullback-Leibler global information criterion. Robustness against early stage instability has been reported for the D-optimality criterion in a polytomous CAT with the…

A criterion-referenced computerized test is expressed as a statistical hypothesis problem. This admits that it can be studied by using the theory of optimal design. The power function of the statistical test is used as a criterion function when designing the test. A formal proof is provided showing that all items should have the same item…

A new sharing item response theory (SIRT) model is presented that explicitly models the effects of sharing item content between informants and test takers. This model is used to construct adaptive item selection and scoring rules that provide increased precision and reduced score gains in instances where sharing occurs. The adaptive item selection…

Test-item writing efforts typically results in item pools with an undesirable correlational structure between the content attributes of the items and their statistical information. If such pools are used in computerized adaptive testing (CAT), the algorithm may be forced to select items with less than optimal information, that violate the content…