@article {2145, title = {A Feedback Control Strategy for Enhancing Item Selection Efficiency in Computerized Adaptive Testing}, journal = {Applied Psychological Measurement}, volume = {30}, number = {2}, year = {2006}, pages = {84-99}, 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\&$\#$39;s estimated and true \θ levels, nonoptimal item selection is a likely result. Nevertheless, examinee response behavior consistent with optimal item selection can be predicted using item response theory (IRT), without knowledge of an examinee\&$\#$39;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.

}, doi = {10.1177/0146621605282774}, url = {http://apm.sagepub.com/content/30/2/84.abstract}, author = {Weissman, Alexander} }