面向难以测量目标的进展监测的潜在过程模型：在心理健康与在线教育评估中的应用
A latent process model for monitoring progress towards hard-to-measure targets, with applications to mental health and online educational assessments
AI总结：
本文提出一种潜在过程模型，通过将个体与变量嵌入共享度量空间来监测难以测量目标的进展，并应用于心理健康和在线教育评估，为弱势群体提供新视角。
摘要：
近期向远程学习和工作的转变加剧了长期存在的问题，例如监测个体心理健康以及学生向学习目标进展的问题。我们引入一种新颖的潜在过程模型，旨在监测个体向感兴趣的难以测量目标的进展，该目标通过一组变量进行测量。该潜在过程模型基于将个体和测量目标进展的变量共同嵌入到一个共享度量空间的思想，该空间被解释为捕捉个体与变量之间交互的交互图。个体与目标嵌入在同一度量空间中的事实有助于评估个体向目标的进展。我们通过模拟和应用证明，该潜在过程模型能够为弱势亚群体中的心理健康和在线教育评估提供新颖的视角。
英文摘要：
The recent shift to remote learning and work has aggravated long-standing problems, such as the problem of monitoring the mental health of individuals and the progress of students towards learning targets. We introduce a novel latent process model with a view to monitoring the progress of individuals towards a hard-to-measure target of interest, measured by a set of variables. The latent process model is based on the idea of embedding both individuals and variables measuring progress towards the target of interest in a shared metric space, interpreted as an interaction map that captures interactions between individuals and variables. The fact that individuals are embedded in the same metric space as the target helps assess the progress of individuals towards the target. We demonstrate, with the help of simulations and applications, that the latent process model enables a novel look at mental health and online educational assessments in disadvantaged subpopulations.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
