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Computerized classification testing under the one-parameter logistic response model with ability-based guessing

机译：基于能力猜测的一参数逻辑响应模型下的计算机分类测试

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The one-parameter logistic model with ability-based guessing (1PL-AG) has been recently developed to account for effect of ability on guessing behavior in multiple-choice items. In this study, the authors developed algorithms for computerized classification testing under the 1PL-AG and conducted a series of simulations to evaluate their performances. Four item selection methods (the Fisher information, the Fisher information with a posterior distribution, the progressive method, and the adjusted progressive method) and two termination criteria (the ability confidence interval [ACI] method and the sequential probability ratio test [SPRT]) were developed. In addition, the Sympson-Hetter online method with freeze (SHOF) was implemented for item exposure control. Major results include the following: (a) when no item exposure control was made, all the four item selection methods yielded very similar correct classification rates, but the Fisher information method had the worst item bank usage and the highest item exposure rate; (b) SHOF can successfully maintain the item exposure rate at a prespecified level, without compromising substantial accuracy and efficiency in classification; (c) once SHOF was implemented, all the four methods performed almost identically; (d) ACI appeared to be slightly more efficient than SPRT; and (e) in general, a higher weight of ability in guessing led to a slightly higher accuracy and efficiency, and a lower forced classification rate.
机译：最近开发了具有基于能力的猜测的单参数逻辑模型（1PL-AG），以考虑能力对多项选择项中猜测行为的影响。在这项研究中，作者开发了在1PL-AG下用于计算机分类测试的算法，并进行了一系列仿真以评估其性能。四种项目选择方法（Fisher信息，具有后验分布的Fisher信息，渐进方法和调整后的渐进方法）和两种终止准则（能力置信区间[ACI]方法和顺序概率比检验[SPRT]）被开发。此外，还采用了Sympson-Hetter在线冻结方法（SHOF）进行物品暴露控制。主要结果如下：（a）在没有进行项目暴露控制的情况下，所有四种项目选择方法均产生非常相似的正确分类率，但Fisher信息方法的项目库使用率最差，项目暴露率最高; （b）SHOF可以成功地将物品的暴露率保持在预定水平，而不会影响分类的实质准确性和效率; （c）实施SHOF后，这四种方法的执行几乎完全相同; （d）ACI似乎比SPRT更有效率; （e）通常，较高的猜测能力会导致较高的准确性和效率，以及较低的强制分类率。

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