Advances in CD-CAT: The General Nonparametric Item Selection Method
Advances in CD-CAT: The General Nonparametric Item Selection Method
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CD-CAT 的进展：通用非参数项目选择方法
DOI：
10.1007/s11336-021-09792-z
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发表时间：
2021
期刊：
影响因子：
3
通讯作者：
Yuan
中科院分区：
文献类型：
--
作者：
Chia;Yuan
计算机自适应测试（CAT）与传统的纸笔测试相比，具有较高的估计效率和准确性。专门用于认知诊断的CAT （CD-CAT）具有相同的优势，并已被视为促进认知诊断（CD）评估在教育实践中的使用的工具。一个强大的项目选择方法是CD-CAT项目成功的关键，迄今为止，各种参数化的项目选择方法已经被提出并得到了很好的研究。然而，这些参数化方法都需要大样本，以确保对题库中的项目进行高精度校准。因此，目前，在小规模教育环境中，如教室，实施参数化方法仍然具有挑战性。针对这一问题，Chang、Chiu和Tsai （appll Psychol Meas 43:543-561, 2019）提出了不需要参数校准的非参数项目选择（NPS）方法，该方法在只有小样本或没有校准样本的情况下优于参数方法。然而，NPS方法并非没有局限性；当数据符合复杂模型时，需要额外的假设来保证属性概要的一致估计。为了弥补这一缺点，本研究提出了通用非参数项目选择（GNPS）方法，该方法结合了新开发的通用NPC （GNPC）方法（Chiu et al. in Psychometrika 83:355-375, 2018）作为分类工具。GNPS方法中包含GNPC方法放宽了对NPS方法施加的假设。因此，GNPS方法可以用于任何模型或多个模型，而不会放弃小样本技术的优势。本文提出的定理1支持了GNPS方法在CD-CAT系统中使用的合法性。仿真结果表明，在标定样本较小的情况下，GNPS方法优于其他参数方法，验证了GNPS方法的有效性和有效性。
Computerized adaptive testing (CAT) is characterized by its high estimation efficiency and accuracy, in contrast to the traditional paper-and-pencil format. CAT specifically for cognitive diagnosis (CD-CAT) carries the same advantages and has been seen as a tool for advancing the use of cognitive diagnosis (CD) assessment for educational practice. A powerful item selection method is the key to the success of a CD-CAT program, and to date, various parametric item selection methods have been proposed and well-researched. However, these parametric methods all require large samples, to secure high-precision calibration of the items in the item bank. Thus, at present, implementation of parametric methods in small-scale educational settings, such as classroom, remains challenging. In response to this issue, Chang, Chiu, and Tsai (Appl Psychol Meas 43:543–561, 2019) proposed the nonparametric item selection (NPS) method that does not require parameter calibration and outperforms the parametric methods for settings with only small or no calibration samples. Nevertheless, the NPS method is not without limitations; extra assumptions are required to guarantee a consistent estimator of the attribute profiles when data conform to complex models. To remedy this shortcoming, the general nonparametric item selection (GNPS) method that incorporates the newly developed general NPC (GNPC) method (Chiu et al. in Psychometrika 83:355–375, 2018) as the classification vehicle is proposed in this study. The inclusion of the GNPC method in the GNPS method relaxes the assumptions imposed on the NPS method. As a result, the GNPS method can be used with any model or multiple models without abandoning the advantage of being a small-sample technique. The legitimacy of using the GNPS method in the CD-CAT system is supported by Theorem 1 proposed in the study. The efficiency and effectiveness of the GNPS method are confirmed by the simulation study that shows the outperformance of the GNPS method over the compared parametric methods when the calibration samples are small.