关键词: 知识追踪, 图神经网络, 自注意力, 概念关联, 遗忘机制

Abstract: Tracking students' historical interactions to predict future performance is a critical research focus in the field of Knowledge Tracing (KT). Recent KT approaches aim to explore students' learning patterns and evolving knowledge states to provide personalized learning guidance but often overlook the richness of exercises themselves. Additionally, with the emergence of new disciplines and interdisciplinary fields, Graph Neural Network (GNN)-based KT methods face inherent challenges, such as broadening the scope of concept associations and modeling students' learning behaviors. To address these challenges, a novel KT model, Concept association memory network by fusing graph attention knowledge tracking (GTMKT), was proposed. GTMKT models students' exercise records to trace their knowledge states and captures the global features of relevant concepts from the exercise-concept graph. Forgetting gates and higher-order information extraction were incorporated into the model to realistically simulate students' exercise processes. Through extensive experiments, compared with seven models, including Graph-based Knowledge Tracing (GKT) and Memory-Enhanced Knowledge Tracing (DGMN), on the Junyi, ASSIST09, and Static2011 datasets, the AUC and ACC performance improved by approximately 2.1% and 2.4%, respectively, demonstrating that GTMKT outperforms the baseline models on well-established knowledge structure datasets.

Key words: Knowledge Tracing&#40, KT&#41, graph neural network, self attention