Reading is foundational for educational, employment, and economic outcomes, but a persistent proportion of students globally struggle to develop adequate reading skills. Some countries promote digital tools to support reading development, alongside regular classroom instruction. Such tools generate rich log data capturing students' behaviour and performance. This study proposes a dynamic cognitive diagnostic modeling (CDM) framework based on restricted latent class models to trace students' time-varying skills mastery using log files from digital tools. Unlike traditional CDMs that require expert-defined skill-item mappings (Q-matrix), our approach jointly estimates the Q-matrix and latent skill profiles, integrates log-derived covariates (e.g., reattempts, response times, counts of mastered items) and individual characteristics, and models transitions in mastery using a Bayesian estimation approach. Applied to real-world data, the model demonstrates practical value in educational settings by effectively uncovering individual skill profiles and the skill-item mappings. Simulation studies confirm robust recovery of Q-matrix structures and latent profiles with high accuracy under varied sample sizes, item counts and different sparsity of Q-matrices. The framework offers a data-driven, time-dependent restricted latent class modeling approach to understanding early reading development.

翻译：阅读能力是教育、就业和经济成果的基础，但全球范围内仍有相当比例的学生难以发展出足够的阅读技能。一些国家在常规课堂教学之外，还推广数字工具以支持阅读能力发展。此类工具能够生成记录学生行为和表现的丰富日志数据。本研究提出了一种基于受限潜在类别模型的动态认知诊断建模框架，利用数字工具的日志文件追踪学生随时间变化的技能掌握情况。与需要专家定义技能-题目映射的传统认知诊断模型不同，我们的方法联合估计Q矩阵和潜在技能轮廓，整合日志衍生的协变量以及个体特征，并采用贝叶斯估计方法对掌握状态的转移进行建模。应用于真实世界数据时，该模型通过有效揭示个体技能轮廓和技能-题目映射，展现了其在教育场景中的实用价值。模拟研究证实，在不同样本量、题目数量及Q矩阵稀疏度条件下，模型均能以高精度稳健地恢复Q矩阵结构和潜在轮廓。该框架为理解早期阅读发展提供了一种数据驱动、时间依赖的受限潜在类别建模方法。