Refined tracking allows students and teachers to more accurately understand students’ learning growth. To provide refined learning tracking with longitudinal diagnostic assessment, this article proposed a new model by incorporating probabilistic logic into longitudinal diagnostic modeling. Specifically, probabilistic attributes were used instead of binary attributes to model the latent variables that affect students’ performance. Thus, in the proposed model, attribute‐level growth can be quantified in a more refined manner. The feasibility of the proposed model was examined using simulated data. The results mainly indicated that the model parameters for the proposed model could be well recovered. An empirical example was conducted to illustrate the applicability and advantages of the proposed model. The results mainly indicated that when distinguishing the level of students, the diagnostic results of the proposed model and the conventional longitudinal diagnostic model for binary attributes displayed a high degree of consistency; however, the former could provide more refined description of growth and a better model‐data fit than the latter.

中文翻译：

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具有纵向概率诊断模型的精细学习跟踪

完善的跟踪功能使学生和教师可以更准确地了解学生的学习发展情况。为了通过纵向诊断评估提供完善的学习跟踪，本文提出了一种将概率逻辑纳入纵向诊断建模的新模型。具体而言，使用概率属性代替二进制属性来建模影响学生成绩的潜在变量。因此，在提出的模型中，可以以更精细的方式量化属性级别的增长。使用模拟数据检查了所提出模型的可行性。结果主要表明，该模型的模型参数可以很好地恢复。进行了一个经验例子来说明所提出模型的适用性和优点。研究结果主要表明，在区分学生水平时，所提模型与常规纵向二元属性诊断模型的诊断结果具有较高的一致性。但是，前者可以提供比后者更好的增长描述和更好的模型数据拟合。