Title | A Feedback Control Strategy for Enhancing Item Selection Efficiency in Computerized Adaptive Testing |
Publication Type | Journal Article |
Year of Publication | 2006 |
Authors | Weissman, A |
Journal | Applied Psychological Measurement |
Volume | 30 |
Number | 2 |
Pagination | 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'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. |
URL | http://apm.sagepub.com/content/30/2/84.abstract |
DOI | 10.1177/0146621605282774 |