Development and Application of an Exploratory Reduced Reparameterized Unified Model
Development and Application of an Exploratory Reduced Reparameterized Unified Model
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DOI：
10.3102/1076998618791306
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发表时间：
2019-02-01
影响因子：
2.4
通讯作者：
Chen, Yinghan
中科院分区：
文献类型：
--
作者：
Culpepper, Steven Andrew;Chen, Yinghan
探索性认知诊断模型（CDM）估计Q矩阵，Q矩阵是一个二元矩阵，表示对每个项目做出肯定回答所需的属性。估计Q是改进清洁发展机制分类和扩大其应用的下一个重要步骤。以前的研究主要集中在限制性确定性输入，噪声和门模型的探索性版本，研究需要开发更灵活的CDM探索性方法。我们认为贝叶斯方法估计的探索性版本的更灵活的减少重新参数化的统一模型（rRUM）。我们发现，估计rRUM Q矩阵是复杂的Q和rRUM项目参数的元素之间的混淆。一个贝叶斯框架，准确地恢复Q使用钉板项目参数之前，选择所需的属性为每个项目。我们目前的Monte Carlo模拟研究，证明所开发的算法改进先验贝叶斯方法估计rRUM Q矩阵。我们将所开发的方法应用于英语水平证书考试数据集。结果提供了证据的五个属性与部分有序的属性层次结构。
Exploratory cognitive diagnosis models (CDMs) estimate the Q matrix, which is a binary matrix that indicates the attributes needed for affirmative responses to each item. Estimation of Q is an important next step for improving classifications and broadening application of CDMs. Prior research primarily focused on an exploratory version of the restrictive deterministic-input, noisy-and-gate model, and research is needed to develop exploratory methods for more flexible CDMs. We consider Bayesian methods for estimating an exploratory version of the more flexible reduced reparameterized unified model (rRUM). We show that estimating the rRUM Q matrix is complicated by a confound between elements of Q and the rRUM item parameters. A Bayesian framework is presented that accurately recovers Q using a spike-slab prior for item parameters to select the required attributes for each item. We present Monte Carlo simulation studies, demonstrating the developed algorithm improves upon prior Bayesian methods for estimating the rRUM Q matrix. We apply the developed method to the Examination for the Certificate of Proficiency in English data set. The results provide evidence of five attributes with a partially ordered attribute hierarchy.