Q矩阵是认知诊断的基础,Q矩阵标定错误会影响被试分类的准确性。本研究基于非参数角度开发了不受模型限制的Q矩阵修正方法,并与已有参数化方法进行了比较。研究结果发现：（1）非参数的PLM方法可实现各模型下的Q矩阵修正,方法具有计算简单、方便使用且不受模型限制的特征。（2）非参数的PLM方法表现明显优于stepwise方法,而GDI方法和RSS方法的表现最差。（3）实证数据分析表明,PLM方法修正后的Q矩阵具有更好的相对拟合和绝对拟合结果。
Abstract
Different from the item response models that postulate a single underlying proficiency, cognitive diagnostic assessments (CDAs) can provide fine-grained diagnostic information on students' knowledge states to support classroom training. The Q-matrix, which links each item to a set of cognitive skills, is necessary to infer students' knowledge states and serves as the foundation for cognitive diagnosis. In reality, domain experts often construct the Q-matrix, which is undoubtedly influenced by their subjective tendencies and, to a significant extent, may contain certain misspecifications. In order to guarantee the accuracy of the Q-matrix, several Q-matrix validation methods have been put forth to find and fix incorrect entries in the known Q-matrix supplied by experts. However, the majority of the currently used methods are parametric methods that are constrained by the sample size or cognitive diagnosis model (CDM).To address this concern, this work developed a generalized nonparametric method to validate the Q-matrix based on the response data, which can be applied to a broad class of cognitive diagnosis models(CDMs).
A general nonparametric classification approach (GNPC) has been proposed by Chiu et al.(2018) and can be applied when the model is saturated, and the sample size is limited. Besides, Chiu (2013) also proposed a nonparametric Q-matrix validation method by minimizing the residual sum of squares (RSS) between the observed responses and ideal responses, which can only be used with the deterministic input, noisy and gate (DINA) and deterministic input, noisy or gate (DINO) models. Inspired by these two studies, a generalized nonparametric Q-validation method has been proposed in this paper, and the steps of the method are as follows. First, using the GNPC approach, it is possible to estimate the attribute patterns of each examinee. The ideal response of every examinee on each item can therefore be calculated in the saturated model using the GNPC method. For each item, the residual sum of squares (RSS) of the ideal response and the observed response can be calculated and the item with the highest RSS will be validated first. For the q-vector of an item, a statistic L was constructed and penalized with the sample size and the number of attributes. Finally, the q-vector corresponding to the smallest penalty L is selected as the q-vector of the item.
The feasibility and effectiveness of the proposed method were evaluated by simulated data generated under various conditions and an application example in real data. The performance of the method in this research was compared with the RSS method, GDI method (de la Torre & Chiu, 2016), and stepwise method(Ma & de la Torre, 2020). In the simulation studies, a number of pertinent variables were taken into account, such as the percentage of misspecifications, cognitive diagnostic models, sample sizes, and attribute distributions. Results demonstrate that (1) In general, the proposed nonparametric method outperforms the GDI and the stepwise method, and can be used with a broad class of cognitive diagnosis models (CDMs). (2)In the small sample (N<200), the performance of the proposed nonparametric method is the best of the three methods, followed by the stepwise method, and the GDI method performs the worst. (3) The empirical data analysis shows that the Q-matrix, validated by the nonparametric method, has better relative fitting and absolute fitting results.
关键词
认知诊断 /
Q矩阵 /
非参数方法 /
GDI方法
Key words
cognitive diagnosis /
Q-matrix /
nonparametric method /
gdi method
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参考文献
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基金
*本研究得到教育部人文社科项目（22YJC190021）、江西省社会科学基金项目（23JY15）、江西省高校人文社会科学研究项目（XL21207）、江西省教育厅科技项目（GJJ2200358）和国家自然科学基金（32300942, 32160203, 62167004, 31960186）的资助
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