Abstract

This study aims to address the limitations of static analysis in tracking the temporal dynamics of programming errors among learners. A Temporal Error Evolution Knowledge Graph (TEE-KG) framework is proposed, integrating multi-source data (code submissions, debugging logs, and learning sequences) with temporal reasoning mechanisms. Using a dataset of 1,246 undergraduate students’ Python learning trajectories over 16 weeks, the framework was validated via comparative experiments with baseline models (LSTM, static KG). Results showed TEE-KG outperformed baselines in error trend prediction (MAE=0.72 vs. 1.31/1.05) and root cause identification (F1=0.89 vs. 0.76/0.81). The findings demonstrate that dynamic knowledge graphs enable granular visualization of error evolution, providing actionable insights for personalized programming education.

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