Knowledge tracing (KT) which aims at predicting learner's knowledge mastery plays an important role in the computer-aided educational system. In recent years, many deep learning models have been applied to tackle the KT task, which has shown promising results. However, limitations still exist. Most existing methods simplify the exercising records as knowledge sequence, which fails to explore rich information existed in exercise texts. Besides, the latent hierarchical graph nature of exercises and knowledge remains unexplored. Thus, in this paper, we propose a hierarchical graph knowledge tracing model framework (HGKT) which can leverage the advantages of hierarchical exercise graph and of sequence model to enhance the ability of knowledge tracing. Besides, we introduce the concept of problem schema to better represent a group of similar exercises and propose a hierarchical graph neural network to learn representations of problem schemas. Moreover, in the sequence model, we employ two attention mechanisms to highlight important historical states of students. In the testing stage, we present a K\&S diagnosis matrix that could trace the transition of mastery of knowledge and problem schema, which can be more easily applied to different applications. Extensive experiments show the effectiveness and interpretability of our proposed models.

中文翻译：

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为知识追踪引入带有分层练习图的问题模式

旨在预测学习者知识掌握程度的知识追踪（KT）在计算机辅助教育系统中起着重要作用。近年来，许多深度学习模型已被应用于解决 KT 任务，并取得了可喜的成果。但是，限制仍然存在。现有的方法大多将练习记录简化为知识序列，未能挖掘练习文本中存在的丰富信息。此外，练习和知识的潜在层次图性质仍未得到探索。因此，在本文中，我们提出了一种分层图知识追踪模型框架（HGKT），它可以利用分层练习图和序列模型的优点来增强知识追踪的能力。除了，我们引入了问题模式的概念来更好地表示一组相似的练习，并提出了一个层次图神经网络来学习问题模式的表示。此外，在序列模型中，我们采用了两种注意力机制来突出学生的重要历史状态。在测试阶段，我们提出了一个 K\&S 诊断矩阵，它可以跟踪知识掌握和问题模式的转变，可以更容易地应用于不同的应用程序。大量实验表明我们提出的模型的有效性和可解释性。S 诊断矩阵，可以追踪知识掌握和问题图式的转变，可以更容易地应用于不同的应用程序。大量实验表明我们提出的模型的有效性和可解释性。S 诊断矩阵，可以追踪知识掌握和问题图式的转变，可以更容易地应用于不同的应用程序。大量实验表明我们提出的模型的有效性和可解释性。