Cheng Ying, Diao Qi, Behrens John T
Department of Psychology, University of Notre Dame, 118 Haggar Hall, Notre Dame, IN, 46556, USA.
Pacific Metrics Corporation, Monterey, CA, USA.
Behav Res Methods. 2017 Apr;49(2):502-512. doi: 10.3758/s13428-016-0712-6.
In this article, we propose a simplified version of the maximum information per time unit method (MIT; Fan, Wang, Chang, & Douglas, Journal of Educational and Behavioral Statistics 37: 655-670, 2012), or MIT-S, for computerized adaptive testing. Unlike the original MIT method, the proposed MIT-S method does not require fitting a response time model to the individual-level response time data. It is also computationally efficient. The performance of the MIT-S method was compared against that of the maximum information (MI) method in terms of measurement precision, testing time saving, and item pool usage under various item response theory (IRT) models. The results indicated that when the underlying IRT model is the two- or three-parameter logistic model, the MIT-S method maintains measurement precision and saves testing time. It performs similarly to the MI method in exposure control; both result in highly skewed item exposure distributions, due to heavy reliance on the highly discriminating items. If the underlying model is the one-parameter logistic (1PL) model, the MIT-S method maintains the measurement precision and saves a considerable amount of testing time. However, its heavy reliance on time-saving items leads to a highly skewed item exposure distribution. This weakness can be ameliorated by using randomesque exposure control, which successfully balances the item pool usage. Overall, the MIT-S method with randomesque exposure control is recommended for achieving better testing efficiency while maintaining measurement precision and balanced item pool usage when the underlying IRT model is 1PL.
在本文中，我们提出了一种用于计算机自适应测试的最大信息每时间单位方法（MIT；Fan、Wang、Chang和Douglas，《教育与行为统计杂志》37：655 - 670，2012年）的简化版本，即MIT - S。与原始的MIT方法不同，所提出的MIT - S方法不需要将响应时间模型拟合到个体层面的响应时间数据。它在计算上也很高效。在各种项目反应理论（IRT）模型下，就测量精度、节省测试时间和项目库使用情况而言，将MIT - S方法的性能与最大信息（MI）方法进行了比较。结果表明，当基础IRT模型是两参数或三参数逻辑斯蒂模型时，MIT - S方法保持测量精度并节省测试时间。在曝光控制方面，它的表现与MI方法类似；由于严重依赖高区分度项目，两者都会导致项目曝光分布高度偏斜。如果基础模型是单参数逻辑斯蒂（1PL）模型，MIT - S方法保持测量精度并节省大量测试时间。然而，它对节省时间项目的严重依赖导致项目曝光分布高度偏斜。通过使用类似随机的曝光控制可以改善这一弱点，这种控制成功地平衡了项目库的使用。总体而言，当基础IRT模型为1PL时，建议使用具有类似随机曝光控制的MIT - S方法，以在保持测量精度和平衡项目库使用的同时实现更好的测试效率。
