增强算法及其在认知诊断中的作用

Shu-liang Ding, Fen Luo, Wen-yi Wang, Xiaofeng Yu, Jianhua Xiong
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引用次数: 0

摘要

增强算法和约简算法都可以在测试中得到非零的知识状态向量。特别地,基于可达矩阵的增广算法可以推导出q矩阵及其非零列的结构,从而证明q矩阵列的集合形成一个代数结构(格)。分别应用基于可达性矩阵和测试q -矩阵的增强算法,我们可以得到测试q -矩阵的理论构造效度(即测试q -矩阵拟合认知模型的程度),并利用结果来评价测试质量。我们还可以在构建和评估认知模型以及开发认知诊断模型时使用该算法。此外,增强算法及其反向算法(约简算法)适合于对改造数据进行分析和评价。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The Augment Algorithm and its Role in Cognitive Diagnosis
Both the augment algorithm and the reduction algorithm can be used to obtain non-zero knowledge states vector in testing. In particular, the augment algorithm based on the reachability matrix can imply the structure of the Q-matrix and its non-zero columns, thus proving the set of Q-matrix columns forms an algebraic structure (Lattice). Applying the augment algorithm based on the reachability matrix and the test Q-matrix, respectively, we can obtain the theoretical construct validity of the test Q-matrix (i.e., the degree of the test Q-matrix fitting the cognitive model) and use the results to evaluate test quality. We can also use the algorithm when constructing and evaluating cognitive models, as well as when developing cognitive diagnostic models. Moreover, the augment algorithm and its reverse algorithm (reduction algorithm) are suitable for analyzing and evaluating retrofitting data.
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