AMBER: Adaptive Meta Balanced Paradigm for Heterogeneous Graph-Based Knowledge Tracing

Knowledge Tracing (KT) is a fundamental task in personalized education, aiming to predict student performance by modeling their evolving concept mastery. Recent state-of-the-art approaches adopt multi-graph architectures to capture diverse concept and behavior relations. However, such models often suffer from graph imbalance, where one graph branch dominates training, undermining the benefits of structural integration. To address this, we propose AMBER (Adaptive Meta-Balanced Ensemble Representation learning), a KT framework designed to promote balanced learning across heterogeneous graph structures. AMBER introduces an external dual-graph teacher to guide the learning of ensemble representations. As the teacher itself may encode graph imbalance bias, we further incorporate a meta-distillation strategy that adaptively adjusts the teacher using student feedback, amplifying signals beneficial to underperforming branches. In addition, an adaptive graph rebalancing strategy is introduced to balance the optimization of different graph branches in real time, preventing dominance by any single structure. Experiments on three real-world datasets show that AMBER consistently outperforms competitive baselines. By promoting balanced optimization across graphs, AMBER enables more effective integration of heterogeneous learning signals in KT, providing a robust and scalable solution for personalized education. Code is available at https://github.com/AMBER2025KT/AMBER2025CIKM. © 2025 the owner/author(s).