Direct Estimation of Diagnostic Classification Model Attribute Mastery Profiles via a Collapsed Gibbs Sampling Algorithm
Direct Estimation of Diagnostic Classification Model Attribute Mastery Profiles via a Collapsed Gibbs Sampling Algorithm
复制标题
通过折叠吉布斯采样算法直接估计诊断分类模型属性掌握概况
DOI：
10.1007/s11336-022-09857-7
复制
发表时间：
2022
期刊：
影响因子：
3
通讯作者：
Templin Jonathan
中科院分区：
文献类型：
--
作者：
Yamaguchi Kazuhiro;Templin Jonathan
本文提出了一种新颖的折叠吉布斯采样算法，该算法边缘化模型参数并直接对诊断分类模型中的潜在属性掌握模式进行采样。该估计方法无需估计模型项参数，从而可以避免模型项参数估计中的边界问题。仿真研究表明，折叠吉布斯采样算法能够准确地恢复各种条件下的真实属性掌握状态。第二次模拟表明，折叠吉布斯采样算法在计算上比 JAGS 实现的另一种 MCMC 采样算法更有效。在对实际数据的分析中，折叠吉布斯采样算法表明与先前研究的结果具有良好的分类一致性。
This paper proposes a novel collapsed Gibbs sampling algorithm that marginalizes model parameters and directly samples latent attribute mastery patterns in diagnostic classification models. This estimation method makes it possible to avoid boundary problems in the estimation of model item parameters by eliminating the need to estimate such parameters. A simulation study showed the collapsed Gibbs sampling algorithm can accurately recover the true attribute mastery status in various conditions. A second simulation showed the collapsed Gibbs sampling algorithm was computationally more efficient than another MCMC sampling algorithm, implemented by JAGS. In an analysis of real data, the collapsed Gibbs sampling algorithm indicated good classification agreement with results from a previous study.