Wang Yutong, Sun Xiaojian, Chong Weifeng, Xin Tao
Collaborative Innovation Center of Assessment for Basic Education Quality, Beijing Normal University, Beijing, China.
School of Mathematics and Statistics, Southwest University, Chongqing, China.
Front Psychol. 2020 Feb 28;11:224. doi: 10.3389/fpsyg.2020.00224. eCollection 2020.
We propose a new method that balances attribute coverage for short-length cognitive diagnostic computerized adaptive testing (CD-CAT). The new method uses the attribute discrimination index (ADI-based method) instead of the number of items that measure each attribute [modified global discrimination index (MGDI)-based method] to balance the attribute coverage. Therefore, the information that each attribute provides can be captured. The purpose of the simulation study was to evaluate the performance of the new method, and the results showed the following: (a) Compared with uncontrolled attribute-balance coverage method, the new method produced a higher mastery pattern correct classification rate (PCCR) and attribute correct classification rate (ACCR) with both the posterior-weighted Kullback-Leibler (PWKL) and the modified PWKL (MPWKL) item selection method. (b) Equalization of ACCR (E-ACCR) based on the ADI-based method leads to better results, followed by the MGDI-based method. The uncontrolled method leads to the worst results regardless of item selection methods. (c) Both the ADI-based and MGDI-based methods produced acceptable examinee qualification rates, regardless of item selection methods, although they were relatively low for the uncontrolled condition.
我们提出了一种新方法，用于平衡短长度认知诊断计算机自适应测试（CD-CAT）中的属性覆盖率。该新方法使用属性区分指数（基于ADI的方法）而非测量每个属性的题目数量（基于修正全局区分指数（MGDI）的方法）来平衡属性覆盖率。因此，每个属性提供的信息能够被捕捉到。模拟研究的目的是评估新方法的性能，结果表明：（a）与无控制的属性平衡覆盖率方法相比，无论是采用后验加权Kullback-Leibler（PWKL）还是修正的PWKL（MPWKL）题目选择方法，新方法都产生了更高的掌握模式正确分类率（PCCR）和属性正确分类率（ACCR）。（b）基于ADI的方法的ACCR均衡化（E-ACCR）产生了更好的结果，其次是基于MGDI的方法。无论采用何种题目选择方法，无控制方法都导致最差的结果。（c）无论采用何种题目选择方法，基于ADI的方法和基于MGDI的方法都产生了可接受的考生合格通过率，尽管在无控制条件下该通过率相对较低。
