认知诊断Q矩阵估计(修正)方法

Q矩阵代表着项目考察的属性, 反映了项目的重要特征, 其正确性是影响认知诊断分类准确性的关键因素。研究Q矩阵估计(修正)方法具有重要价值。首先, 研究从是否采用认知诊断模型将Q矩阵估计(修正)分为基于认知诊断模型视角下的参数化方法和基于统计视角下的非参数方法。然后, 分别从最优项目质量、最优模型数据拟合和参数估计视角对它们进行分类介绍, 评析不同方法的特征和表现、区别与联系、优势与不足。最后, 提出几个未来研究问题：在复杂测验条件下系统比较各种方法; 校准知识状态和参数估计误差、结合多种思路和方法等多角度提出Q矩阵估计(修正)方法; 研究多级评分项目、混合测验模型、属性多级、属性个数未知甚至Q矩阵元素为连续变量等条件下的Q矩阵估计(修正)方法。

Q-matrix estimation (validation) methods for cognitive diagnosis

The Q-matrix, which represents important item characteristics by mapping attributes to items has been proved to be the core factor affecting the accuracy of cognitive diagnostic classification. It is of great value to study the methods of Q-matrix estimation (validation). First, the existing methods of Q-matrix estimation and validation are classified into 1) parameterized methods in the CDM perspective, including item differentiation, model-data fit index and parameter estimation; and 2) non-parametric methods in the statistical perspective, including the distance between observed and expected response vector, abnormal responses index and factor analysis. Then, these methods are introduced in terms of differences and relations, characteristics and performance. The advantages and disadvantages of each method are commented. At last, several future research directions are proposed. It is necessary to compare the Q-matrix estimation (validation) methods systematically under complex test conditions. It is also of vital importance to propose Q-matrix estimation (validation) methods by combining multiple thoughts and ways based on the calibration of knowledge state and parameter estimation error. It is meaningful to further study the Q-matrix estimation (validation) methods for polytomous scoring items, mixed test models, polytomous scoring attributes, unknown number of attributes and even continuous Q-matrix.