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AComparison of Four Item-Selection Methods for Severely Constrained CATs

机译:严重约束CAT的四种项目选择方法的比较

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摘要

This study compared four item-selection procedures developed for use with severely constrained computerized adaptive tests (CATs). Severely constrained CATs refer to those adaptive tests that seek to meet a complex set of constraints that are often not conclusive to each other (i.e., an item may contribute to the satisfaction of several constraints at the same time). The procedures examined in the study included the weighted deviation model (WDM), the weighted penalty model (WPM), the maximum priority index (MPI), and the shadow test approach (STA). In addition, two modified versions of the MPI procedure were introduced to deal with an edge case condition that results in the item selection procedure becoming dysfunctional during a test. The results suggest that the STA worked best among all candidate methods in terms of measurement accuracy and constraint management. For the other three heuristic approaches, they did not differ significantly in measurement accuracy and constraint management at the lower bound level. However, the WPM method appears to perform considerably better in overall constraint management than either the WDM or MPI method. Limitations and future research directions were also discussed.
机译:这项研究比较了为严格约束的计算机自适应测试(CAT)而开发的四种项目选择程序。严重受限的CAT是指那些试图满足通常并不相互确定的复杂约束集的适应性测试(即,一个项目可能同时有助于满足多个约束条件)。研究中检查的程序包括加权偏差模型(WDM),加权惩罚模型(WPM),最大优先级指数(MPI)和影子测试方法(STA)。此外,还引入了MPI程序的两个修改版本,以处理可能导致项目选择程序在测试过程中失灵的边缘情况。结果表明,在测量精度和约束管理方面,STA在所有候选方法中效果最好。对于其他三种启发式方法,它们在下限级别的测量准确性和约束管理方面没有显着差异。但是,与WDM或MPI方法相比,WPM方法在总体约束管理方面的表现似乎要好得多。还讨论了局限性和未来的研究方向。

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