一种广义的非参数化Q矩阵修正方法  

A Generalized Nonparametric Q-Matrix Validation Method

作　　者：汪大勋 肖清文 谭青蓉 蔡艳[1] 涂冬波[1] Wang Daxun;Xiao Qingwen;Tan Qingrong;Cai Yan;Tu Dongbo(School of Psychology,Jiangxi Normal University,Nanchang,330022)

机构地区：[1]江西师范大学心理学院,南昌330022

出　　处：《心理科学》2023年第5期1237-1245,共9页Journal of Psychological Science

基　　金：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);国家自然科学基金(32300942,32160203,62167004,31960186)的资助。

摘　　要：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。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 c

关 键 词：认知诊断 Q矩阵 非参数方法 GDI方法 

分 类 号：B842.1[哲学宗教—基础心理学]