  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.
