Knowledge Tracing (KT) aims to determine whether students will respond correctly to the next question, which is a crucial task in intelligent tutoring systems (ITS). In educational KT scenarios, transductive ID-based methods often face severe data sparsity and cold start problems, where interactions between individual students and questions are sparse, and new questions and concepts consistently arrive in the database. In addition, existing KT models only implicitly consider the correlation between concepts and questions, lacking direct modeling of the more complex relationships in the heterogeneous graph of concepts and questions. In this paper, we propose a Structure-aware Inductive Knowledge Tracing model with large language model (dubbed SINKT), which, for the first time, introduces large language models (LLMs) and realizes inductive knowledge tracing. Firstly, SINKT utilizes LLMs to introduce structural relationships between concepts and constructs a heterogeneous graph for concepts and questions. Secondly, by encoding concepts and questions with LLMs, SINKT incorporates semantic information to aid prediction. Finally, SINKT predicts the student's response to the target question by interacting with the student's knowledge state and the question representation. Experiments on four real-world datasets demonstrate that SINKT achieves state-of-the-art performance among 12 existing transductive KT models. Additionally, we explore the performance of SINKT on the inductive KT task and provide insights into various modules.

翻译：知识追踪旨在预测学生对下一道问题的回答是否正确，这是智能教学系统中的关键任务。在教育知识追踪场景中，基于ID的直推式方法常面临严重的数据稀疏性和冷启动问题，即学生与题目间的交互稀疏，且数据库中持续出现新题目和新概念。此外，现有知识追踪模型仅隐式考虑概念与题目间的关联，缺乏对概念-题目异质图中更复杂关系的直接建模。本文提出一种基于大语言模型的结构感知归纳知识追踪模型（简称SINKT），首次引入大语言模型并实现归纳式知识追踪。首先，SINKT利用大语言模型引入概念间的结构关系，构建概念与题目的异质图。其次，通过大语言模型对概念和题目进行编码，SINKT融合语义信息辅助预测。最后，SINKT通过交互学生知识状态与题目表征来预测学生对目标题目的作答情况。在四个真实数据集上的实验表明，SINKT在12种现有直推式知识追踪模型中取得了最先进的性能。此外，我们探索了SINKT在归纳式知识追踪任务上的表现，并对各模块的作用机制进行了深入分析。