In learning diagnostic assessments, the attribute hierarchy specifies a sequential network of interrelated attribute mastery processes, which makes a test blueprint consistent with the cognitive theory. One of the most important functions of attribute hierarchy is to guide or limit the developmental direction of students and then form a hierarchical learning trajectory. To address the issue that the existing longitudinal learning diagnosis models cannot track the development of hierarchical attributes, this study proposes a new hierarchical longitudinal learning diagnostic modeling approach and two sample models. Compared to the longitudinal learning diagnosis models that do not consider the attribute hierarchy, the proposed models, by taking the sequential mastery tree into account, can accommodate various attribute hierarchies and simultaneously track an individual's learning developmental trajectory. An empirical study was conducted to illustrate the advantages of the proposed model. The results mainly indicated that the proposed model can properly diagnose the development of hierarchical attributes in longitudinal assessments. A simulation study was further conducted to explore the model parameter recovery of the proposed models.

中文翻译：

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具有分层学习轨迹的纵向诊断模型

在学习诊断评估中，属性层次结构指定了相互关联的属性掌握过程的顺序网络，这使得测试蓝图与认知理论一致。属性层次最重要的功能之一是引导或限制学生的发展方向，进而形成层次化的学习轨迹。针对现有纵向学习诊断模型无法跟踪层次属性发展的问题，本研究提出了一种新的层次纵向学习诊断建模方法和两个样本模型。与不考虑属性层次的纵向学习诊断模型相比，所提出的模型通过考虑顺序掌握树，可以适应各种属性层次，同时跟踪个人的学习发展轨迹。进行了实证研究以说明所提出模型的优点。结果主要表明，所提出的模型可以正确诊断纵向评估中分层属性的发展。进一步进行了模拟研究，以探索所提出模型的模型参数恢复。