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AbstractExposure control is an important component of computerized adaptive testing. Exposure control is often used to secure test content by preventing item overexposure, and also to balance item utilization by avoiding item underexposure. Content balancing is another important component of computerized adaptive testing. The shadow-test approach to content balancing has many practical merits, including that it ensures complete satisfaction of all content requirements in every assembled test. Several exposure control methods have been developed over the years. The current study describes modifications to existing exposure control methods for use with shadow-test-based adaptive testing, with the objective of achieving bidirectional exposure control. The performances of the modified methods were compared with respect to item exposure and utilization along with ability estimation accuracy. For the purpose of comparison, a Monte Carlo simulation was performed using realistic item pools and content constraints. Implications for real-world applications are discussed.

AbstractAn extremely efficient MCMC method for Bayesian adaptive testing with polytomous items is explained both conceptually and in mathematical detail. Results from extensive simulation studies with different item pools, polytomous response models, calibration sample sizes, and test lengths are presented. In addition, the case of adaptive testing from pools with a mixture of dichotomous and polytomous items is addressed.