A knowledge tracing model based on heterogeneous exercise graphs and multi-source behavioral feature fusion in blended learning environments

Abstract

With the widespread adoption of blended learning, learner data have evolved from single response sequences to multi-source heterogeneous behaviors spanning pre-class preparation and in-class interaction. This poses new challenges for traditional knowledge tracing methods in modeling the dynamic evolution of students’ cognitive states. To address these challenges, this paper proposes a graph-enhanced and interpretable knowledge tracing model, namely GRIKT, for blended learning scenarios. First, a heterogeneous exercise graph is constructed by integrating exercises, instructional units, learning stages, and exercise types, thereby enabling the model to learn semantically enriched exercise representations. Second, multi-source behavioral features, including pre-class learning behaviors, in-class participation behaviors, and preparation time intervals, are encoded to construct stage-aware behavioral cognitive states. These cognitive states are dynamically injected into the sequential knowledge state through a gated mechanism. In addition, an IRT-based prediction branch is introduced to explicitly model student ability and exercise characteristics, and an attention-based fusion mechanism is used to integrate the deep prediction branch and the IRT branch. Experimental results on a real-world blended learning dataset and the public EdNet dataset show that GRIKT consistently outperforms representative knowledge tracing baselines. On the blended learning dataset, GRIKT achieves an RMSE of 0.3733, an AUC of 0.8482, an ACC of 0.8035, and an F1-score of 0.8715. On EdNet, GRIKT also obtains the best overall performance, with an RMSE of 0.4460, an AUC of 0.7490, an ACC of 0.6886, and an F1-score of 0.7426. The ablation study and interpretability analysis further demonstrate the effectiveness of heterogeneous graph enhancement, stage-aware behavioral cognitive modeling, and IRT-based interpretable prediction. These results indicate that GRIKT can achieve accurate and interpretable knowledge state modeling in blended learning environments.