In在教育领域,认知诊断对于实现个性化学习至关重要。广泛采用的 the field of education, cognitive diagnosis is crucial for achieving personalized learning. The widely adopted DINA (Deterministic Inputs, Noisy And gate) model uncovers students’ mastery of essential skills necessary to answer questions correctly. However, existing DINA-based approaches overlook the dependency between knowledge points, and their model training process is computationally inefficient for large datasets.DINA(确定性输入、噪声和门)模型揭示了学生对正确回答问题所需的基本技能的掌握程度。然而,现有的基于 DINA 的方法忽视了知识点之间的依赖性,并且其模型训练过程对于大型数据集来说计算效率低下。