Wang Wenyi, Song Lihong, Ding Shuliang, Wang Teng, Gao Peng, Xiong Jian
School of Computer and Information Engineering, Jiangxi Normal University, Nanchang, China.
Elementary Education College, Jiangxi Normal University, Nanchang, China.
Front Psychol. 2020 Sep 10;11:2120. doi: 10.3389/fpsyg.2020.02120. eCollection 2020.
Cognitive diagnosis assessment (CDA) can be regarded as a kind of formative assessments because it is intended to promote assessment for learning and modify instruction and learning in classrooms by providing the formative diagnostic information about students' cognitive strengths and weaknesses. CDA has two phases, like a statistical pattern recognition. The first phase is feature generation, followed by classification stage. A Q-matrix, which describes the relationship between items and latent skills, corresponds to the feature generation phase in statistical pattern recognition. Feature generation is of paramount importance in any pattern recognition task. In practice, the Q-matrix is difficult to specify correctly in cognitive diagnosis and misspecification of the Q-matrix can seriously affect the accuracy of the classification of examinees. Based on the fact that any columns of a reduced Q-matrix can be expressed by the columns of a reachability R matrix under the logical OR operation, a semi-supervised learning approach and an optimal design for examinee sampling were proposed for Q-matrix specification under the conjunctive and disjunctive model with independent structure. This method only required subject matter experts specifying a R matrix corresponding to a small part of test items for the independent structure in which the R matrix is an identity matrix. Simulation and real data analysis showed that the new method with the optimal design is promising in terms of correct recovery rates of q-entries.
认知诊断评估（CDA）可被视为一种形成性评估，因为它旨在通过提供有关学生认知优势和劣势的形成性诊断信息来促进学习性评估，并在课堂上调整教学与学习。CDA有两个阶段，类似于统计模式识别。第一阶段是特征生成，随后是分类阶段。一个描述题目与潜在技能之间关系的Q矩阵，对应于统计模式识别中的特征生成阶段。在任何模式识别任务中，特征生成都至关重要。在实践中，Q矩阵在认知诊断中难以正确确定，而Q矩阵的错误指定会严重影响考生分类的准确性。基于在逻辑或运算下简约Q矩阵的任何列都可以由可达性R矩阵的列表示这一事实，针对具有独立结构的合取模型和析取模型下的Q矩阵确定，提出了一种半监督学习方法和考生抽样的优化设计。该方法仅要求学科专家针对独立结构指定与一小部分测试题目相对应的R矩阵，其中R矩阵为单位矩阵。模拟和实际数据分析表明，具有优化设计的新方法在q项的正确恢复率方面很有前景。
