知识状态神经推理的知识追踪模型

针对知识追踪研究忽略知识点之间的拓扑关系，从而限制了知识点状态的表示并最终影响预测效果的问题，提出一种知识状态神经推理知识追踪模型。首先建立知识点关系图和知识点状态图；其次利用扩散模型得到关系图和状态图的投影并完成融合；然后利用逆扩散过程得到融合了知识点关系的整体知识状态表示；最后预测学习者的表现。在模型有效性方面，在若干数据集上对比四个相关模型，所提模型均取得了一定的优势。在可解释性方面，展示了知识状态进化过程与真实答题结果之间的对应关系。在实际应用方面，以该模型为基础的智慧学习环境已分别应用于一门人工智能课程和英语语法课程，并取得了优于对比模型的结果。

Knowledge tracing via neural inference on knowledge states

Aiming at the problem that knowledge tracing research ignores the topological relationship between concepts, thereby limiting the representation of concept states and ultimately affecting the prediction effect, this paper proposed a knowledge state neural reasoning knowledge tracing model. Firstly, it established the concept relationship diagram and concept state diagram; Secondly, it used the diffusion model to obtain the projection of the relationship diagram and the state diagram and complete the fusion. Then, it used the inverse diffusion process to obtain the overall knowledge state representation that integrated the relationship between concepts, and finally predicted the performance of learners. In terms of model effectiveness, comparing four related models on several datasets, the proposed model achieves certain advantages. In terms of interpretability, the proposed model shows the correspondence between the evolution process of knowledge state and the actual answer results. In terms of practical application, the intelligent learning environment based on the model has been applied to an artificial intelligence course and an English grammar course respectively, and has achieved better results than the comparison model.