Bias Reduction of Abilities for Adaptive Online IRT Testing Systems

T. Sakumura, H. Hirose
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引用次数: 13

Abstract

To evaluate the ability of examinees, the item response theory (IRT) gives us useful information. The IRT evaluates the examinee's ability and provides the item characteristics. Recently, we have configured the adaptive online testing using the IRT as one of the CBT (Computer Based Testing) methods, and we have been able to obtain the examinee's ability rating in short testing time. However, when the number of items is too small, we observed the bias of estimates for the ability parameter. In this study, we have performed simulation studies under various conditions, and have known such biases. To circumvent the biases due to the Bayes procedure, we have proposes a simple method to reduce the biases.
自适应在线IRT测试系统的能力偏差减少
项目反应理论(IRT)为评价考生的能力提供了有用的信息。IRT评估考生的能力并提供项目特征。最近,我们将IRT作为CBT (Computer Based testing)方法之一,配置了自适应在线测试,并在较短的测试时间内获得了考生的能力等级。然而,当项目数量过少时,我们观察到能力参数估计的偏差。在本研究中,我们在各种条件下进行了模拟研究,并了解了这种偏差。为了避免贝叶斯过程带来的偏差,我们提出了一种简单的方法来减小偏差。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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