Knowledge Tracing (KT) aims to model a student's learning trajectory and predict performance on the next question. A key challenge is how to better represent the relationships among students, questions, and knowledge concepts (KCs). Recently, graph-based KT paradigms have shown promise for this problem. However, existing methods have not sufficiently explored inter-concept relations, often inferred solely from interaction sequences. In addition, the scale and heterogeneity of KT graphs make full-graph encoding both computationally both costly and noise-prone, causing attention to bleed into student-irrelevant regions and degrading the fidelity of inter-KC relations. To address these issues, we propose a novel framework: Multi-Agent Graph-Enhanced Knowledge Tracing (MAGE-KT). It constructs a multi-view heterogeneous graph by combining a multi-agent KC relation extractor and a student-question interaction graph, capturing complementary semantic and behavioral signals. Conditioned on the target student's history, it retrieves compact, high-value subgraphs and integrates them using an Asymmetric Cross-attention Fusion Module to enhance prediction while avoiding attention diffusion and irrelevant computation. Experiments on three widely used KT datasets show substantial improvements in KC-relation accuracy and clear gains in next-question prediction over existing methods.

翻译：知识追踪（Knowledge Tracing, KT）旨在建模学生的学习轨迹并预测其在下一道题目上的表现。一个核心挑战在于如何更好地表征学生、题目与知识概念（Knowledge Concepts, KCs）之间的关系。近年来，基于图的知识追踪范式在该问题上展现出潜力。然而，现有方法未能充分探索概念间关系，这些关系通常仅从交互序列中推断得出。此外，知识追踪图的规模与异质性使得全图编码在计算上既昂贵又易受噪声干扰，导致注意力扩散至与学生无关的区域，并降低了知识概念间关系的保真度。为解决这些问题，我们提出了一种新颖的框架：多智能体图增强知识追踪（Multi-Agent Graph-Enhanced Knowledge Tracing, MAGE-KT）。该框架通过结合多智能体知识概念关系提取器与学生-题目交互图，构建了一个多视图异质图，以捕捉互补的语义与行为信号。在目标学生历史记录的条件下，框架检索紧凑且高价值的子图，并利用非对称交叉注意力融合模块进行整合，从而在增强预测能力的同时避免注意力扩散与无关计算。在三个广泛使用的知识追踪数据集上的实验表明，本方法在知识概念关系准确性方面相比现有方法有显著提升，并在下一题预测上取得了明显增益。