Technologies that enable individualization for students have significant potential in special education. Computerized Adaptive Testing (CAT) refers to digital assessments that automatically adjust their difficulty level based on students' abilities, allowing for personalized, efficient, and accurate measurement. This article examines whether CAT…

As a method to shorten the test time of the Force Concept Inventory (FCI), we suggest the use of computerized adaptive testing (CAT). CAT is the process of administering a test on a computer, with items (i.e., questions) selected based upon the responses of the examinee to prior items. In so doing, the test length can be significantly shortened.…

For computerized adaptive tests (CATs) to work well, they must have an item pool with sufficient numbers of good quality items. Many researchers have pointed out that, in developing item pools for CATs, not only is the item pool size important but also the distribution of item parameters and practical considerations such as content distribution…

Extended the use of a beta prior in trait estimation to the maximum expected a posteriori (MAP) method of Bayesian estimation. This new method, essentially unbiased MAP, was compared with MAP, essentially unbiased expected a posteriori, weighted likelihood, and maximum-likelihood estimation methods. The new method significantly reduced bias in…

Monte Carlo simulation was used to investigate score bias and information characteristics of Owen's Bayesian adaptive testing strategy, and to examine possible causes of score bias. Factors investigated in three related studies included effects of item discrimination, effects of fixed vs. variable test length, and effects of an accurate prior…