跨国家过程数据中的转移动态与网络结构建模：一种分层多状态生存框架

中文摘要 AI 辅助

基于计算机的评估中的过程数据记录了答题者解决任务时动作的顺序和时机，提供了关于问题解决行为的速度和结构的信息。由于国家-答题组单元格通常较小且不平衡，且观察到的转移支持在不同国家间可能差异显著，因此对跨国过程进行建模具有挑战性。我们提出了一种分层框架，将贝叶斯多状态生存模型与基于网络的转移结构表示相结合。跨国家的部分合并产生了国家特定的协变量和关键动作效应、转移速度以及国家间异质性的估计。后验转移概率网络通过适应异质支持的有向图自动编码器嵌入到公共潜在空间中，并在后验抽取中评估节点角色分布之间的1-Wasserstein距离，以表征全局网络结构，同时传播估计不确定性。我们将该框架应用于国际成人能力评估项目中14个国家的两个问题解决项目。结果揭示了转移速度的跨国异质性、全局网络组织中系统性的答题组差异、各国组内离散度的项目依赖变化，以及围绕共享中间动作的局部路径差异。

英文摘要

Process data from computer-based assessments record the sequence and timing of actions through which respondents solve a task, providing information about both the pace and structure of problem-solving behavior. Modeling such processes across countries is challenging because country-by-response-group cells are often small and unbalanced and the observed transition supports can differ substantially across countries. We propose a hierarchical framework that integrates a Bayesian multi-state survival model with a network-based representation of transition structure. Partial pooling across countries yields country-specific covariate and key-action effects, transition speed, and estimates of between-country heterogeneity. Posterior transition probability networks are embedded in a common latent space using a directed graph auto-encoder adapted to heterogeneous supports, and 1-Wasserstein distances between node-role distributions are evaluated across posterior draws to characterize global network structure while propagating estimation uncertainty. We apply the framework to two problem-solving items from the Programme for the International Assessment of Adult Competencies across 14 countries. The results reveal cross-country heterogeneity in transition speed, systematic response-group differences in global network organization, item-dependent variation in within-group dispersion across countries, and local differences in routing around shared intermediate actions.

发表机构

• Yonsei University（延世大学）
• University of California, Los Angeles（加州大学洛杉矶分校）

机构由 AI 辅助整理，请以论文原文为准。