GCKT: Context-Aware Gating of Heterogeneous Learning Features With Transformer for Cognitive Knowledge Tracing in Intelligent Tutoring Systems

Abstract

With the rapid growth of online education, Knowledge Tracing (KT) has become central to adaptive learning systems. Yet existing models struggle to integrate the multidimensional and heterogeneous signals generated during learning—such as exercise attributes, response behaviors, temporal factors, and hierarchical knowledge structure. Many methods rely on naive feature concatenation or fixed weighting, limiting their ability to capture synergistic interactions among features. We propose Gated full-features Transformer Cognitive Knowledge Tracing (GCKT), a Transformer-based model with a gated fusion mechanism that dynamically integrates multiple inputs. The model first embeds exercise, response correctness, response time, and hierarchical knowledge features (topics and concepts). Topic and concept embeddings are linearly projected into a unified knowledge representation. The exercise, time, correctness, and unified knowledge embeddings are then concatenated and passed through a learnable gating network (linear layer with sigmoid) to produce context-aware importance weights. These weights are applied element-wise to adaptively scale each feature before projection into a fused representation for the sequence encoder, enabling the Transformer to more accurately model the evolution of students’ cognitive states. Extensive experiments on public datasets, including MOOCRadar and Math, show that GCKT consistently outperforms strong baselines—such as DKT, AKT, and SAINT+—on key metrics (AUC and F1), delivering robust gains across settings. The results demonstrate that dynamic, fine-grained feature fusion substantially improves KT performance and that GCKT offers a general, effective approach for modeling complex learning scenarios.