Knowledge Tracing (KT) aims to trace the student’s state of evolutionary mastery for a particular knowledge or concept based on the student’s historical learning interactions with the corresponding exercises. Taking the “exercise-to-concept” relationships as input, several existing methods have been developed to trace and model students’ mastery states. However, these studies face two major shortcomings in KT: 1) they only consider “exercise-to-concept” relationships; 2) the multi-hot embeddings lack interpretability. In order to address the above issues, we propose a Joint graph convolutional network based deep Knowledge Tracing (JKT) framework to model the multi-dimensional relationships of “exercise-to-exercise”, and “concept-to-concept” into graph and fuse them with “exercise-to-concept” relationships. In JKT, it is not only possible to establish connections between exercises under cross-concepts, but also to help capture high-level semantic information and increase the model’s interpretability. In addition, sufficient experiments conducted on four real-world datasets have demonstrated that JKT performs better than the other baseline models. We further illustrate a case study to demonstrate its interpretability for learning analysis

知识追踪 (KT) 旨在根据学生与相应练习的历史学习互动，追踪学生对特定知识或概念的进化掌握状态。以“练习到概念”的关系作为输入，已经开发了几种现有方法来跟踪和模拟学生的掌握状态。然而，这些研究在 KT 中面临两个主要缺点：1）他们只考虑“运动到概念”的关系；2）多热嵌入缺乏可解释性。为了解决上述问题，我们提出了一个Ĵ oint图卷积基于网络的深ķ nowledge牛逼赛车（JKT) 框架将“运动到运动”和“概念到概念”的多维关系建模为图形，并将它们与“运动到概念”的关系融合。在 JKT 中，不仅可以在交叉概念下的练习之间建立联系，还可以帮助捕获高级语义信息并增加模型的可解释性。此外，在四个真实世界数据集上进行的充分实验表明，JKT 的性能优于其他基线模型。我们进一步说明了一个案例研究，以证明其对学习分析的可解释性