01356nas a2200121   4500008003900000245010500039210006900144300001000213490000700223520092800230100002401158856005201182       2006                          d00aA Feedback Control Strategy for Enhancing Item Selection Efficiency in Computerized Adaptive Testing0 aFeedback Control Strategy for Enhancing Item Selection Efficienc  a84-990 v303 a<p>A computerized adaptive test (CAT) may be modeled as a closed-loop system, where item selection is influenced by trait level (&theta;) estimation and vice versa. When discrepancies exist between an examinee&#39;s estimated and true &theta; 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 &theta; 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.</p>
1 aWeissman, Alexander  uhttp://apm.sagepub.com/content/30/2/84.abstract