Enhancing Knowledge Tracing with Residual GRU and k-Attention for Student Response Prediction

Le résumé fourni par la source

Knowledge Tracing (KT) plays a pivotal role in educational research by predicting students’ academic performance and enabling personalized learning interventions through dynamic knowledge state modeling. However, existing KT methods mainly focus on learning outcomes, neglecting the complexity of learning behaviors and underutilizing diverse features, which hinders scalability and interpretability. This study proposes a novel framework, Residual GRU-based Knowledge Tracing with Knowledge Attention (RGAKT), which predicts a student’s next response based on past interactions. The framework aims to enhance temporal dynamics modeling, explainability, and predictive accuracy in knowledge tracing tasks. RGAKT incorporates individual student differences and utilizes a two-layer GRU architecture with residual connections to improve temporal pattern recognition while reducing training complexity. Additionally, a knowledge attention (k-attention) mechanism is introduced to dynamically prioritize relevant interactions, making the model’s decision process more transparent and improving predictive accuracy. Extensive experiments on multiple benchmark datasets demonstrate that RGAKT outperforms state-of-the-art KT models, achieving a 4.48% increase in AUC, a 5.41% improvement in accuracy, a 19.17% reduction in RMSE, and a 3.77% decrease in MAE. These results highlight the model’s superior predictive capabilities and adaptability across diverse learning environments, establishing a new benchmark for KT performance. By integrating GRU-based temporal modeling with dynamic attention mechanisms, the RGAKT framework significantly advances the field of knowledge tracing, driving the development of personalized educational technologies and more adaptive learning systems.

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