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 pursue a Bayesian approach and present simulation results along with applications to mental health and online educational assessments.

翻译：近期远程学习与工作的转型加剧了长期存在的难题，例如个体心理健康监测以及学生学习目标达成进度的追踪问题。我们提出了一种新型潜过程模型，旨在通过一组观测变量，监测个体向特定难以测量目标的进展情况。该模型的核心思想是将个体与衡量目标进展的变量共同嵌入到同一度量空间中，该空间被解读为捕捉个体与变量间交互作用的交互图谱。由于个体与目标变量处于相同的度量空间，这使得评估个体向目标进展的程度成为可能。我们采用贝叶斯方法开展研究，展示了模拟实验结果，并介绍了该模型在心理健康监测与在线教育评估中的实际应用。