Counterfactual Dual-Stream knowledge tracing via high-influence intervention signals

Knowledge Tracing (KT) evaluates students’ dynamic knowledge states from historical interaction sequences. Prior work has largely focused on predictive accuracy, leaving model interpretability underexplored and limiting practical educational applications. In real learning processes, a student’s knowledge state is often shaped by a few critical interactions, termed high-influence intervention signals. Identifying and leveraging these signals is crucial for enhancing both model interpretability and predictive accuracy. To address this challenge, we propose a high-influence intervention-based dual-stream framework for knowledge tracing, termed HIDKT . This method actively identifies high-influence intervention signals through a contrastive learning mechanism. Specifically, we design a dynamic importance scoring mechanism to identify high-influence intervention signals in historical interactions. By intervening on these interactions, we construct a counterfactual interaction stream that runs parallel to the factual interaction stream. Both streams are fed into a shared encoder, where a counterfactual-guided intervention contrastive loss guides the model. This loss explicitly encourages the model to amplify differences induced by high-influence intervention signal interventions in the prediction space, while constraining low-influence perturbations in the latent state space to preserve the stability of the student’s underlying knowledge structure. Extensive experiments on five real-world datasets demonstrate that HIDKT significantly outperforms current state-of-the-art baselines. It improves AUC and ACC by an average of 11.78% and 7.69%, respectively. Further analysis confirms that by explicitly guiding the model to focus on high-influence intervention signals, HIDKT transforms KT from a black-box predictor into a more transparent knowledge tracing tool. This provides a reliable basis for precise educational interventions.