Dynamically Causal-Enhanced Exercise Representations for Adaptive Knowledge Tracing

Knowledge tracing assesses students' mastery and predicts future performance based on historical learning data. Traditional methods primarily rely on predefined static associations between concepts and exercises, which struggle to capture potential causal relationships and dynamic learning patterns, leading to reduced prediction accuracy and limited interpretability. To address these issues, this paper proposes a novel dynamic causal inference framework that integrates Gumbel-Softmax sampling with uncertainty estimation, transforming discrete causal relationships into differentiable continuous weights, and quantifying model uncertainty to enhance robustness against noisy data and improve interpretability. Additionally, inspired by item response theory, the model dynamically adjusts students' latent states by modeling the interaction between student ability and exercises difficulty. Experimental results on three widely-used benchmarks demonstrate that this method achieves state-of-the-art (SOTA) performance in prediction accuracy while also generating interpretable causal relationship weights that provide insights into knowledge acquisition patterns.