When computerized adaptive testing (CAT) is under stringent item exposure control, the precision of trait estimation will substantially decrease. A new item selection method, the dynamic Stratification method based on Dominance Curves (SDC), which is aimed at improving trait estimation, is proposed to mitigate this problem. The objective function of the SDC in item selection is to maximize the sum of test information for all examinees rather than maximizing item information for individual examinees at a single-item administration, as in conventional CAT. To achieve this objective, the SDC uses dominance curves to stratify an item pool into strata with the number being equal to the test length to precisely and accurately increase the quality of the administered items as the test progresses, reducing the likelihood that a high-discrimination item will be administered to an examinee whose ability is not close to the item difficulty. Furthermore, the SDC incorporates a dynamic process for on-the-fly item–stratum adjustment to optimize the use of quality items. Simulation studies were conducted to investigate the performance of the SDC in CAT under item exposure control at different levels of severity. According to the results, the SDC can efficiently improve trait estimation in CAT through greater precision and more accurate trait estimation than those generated by other methods (e.g., the maximum Fisher information method) in most conditions.
Efficiency of Item Selection Method in Variable-length Computerized Adaptive Testing for the Testlet Response Model: Constraint-weighted A-stratification Method
