Abstract
A computerized adaptive test (CAT) may be modeled as a closed-loop system, where item selection is influenced by trait level (θ) estimation and vice versa. When discrepancies exist between an examinee's estimated and true θ levels, nonoptimal item selection is a likely result. Nevertheless, examinee response behavior consistent with optimal item selection can be predicted using item response theory (IRT), without knowledge of an examinee's true θ level, yielding a specific reference point for applying an internal correcting or feedback control mechanism. Incorporating such a mechanism in a CAT is shown to be an effective strategy for increasing item selection efficiency. Results from simulation studies using maximum likelihood (ML) and modal a posteriori (MAP) trait-level estimation and Fisher information (FI) and Fisher interval information (FII) item selection are provided.
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