Multi-Dimensional Knowledge Tracing Based on Multi-Concept and Temporal Embedding

Knowledge tracing is fundamental to intelligent educational systems, as it predicts future learning outcomes by analyzing learners' historical performance related to specific knowledge concepts. While advancements have been made through deep learning techniques in the knowledge tracing domain, traditional research faces two significant limitations: insufficient consideration of temporal factors, which impacts the accurate depiction of knowledge retention and mastery dynamics; and the assumption of a one-to-one correspondence between practice and a single knowledge concept, disregarding the complexity of coexisting multiple knowledge concepts. To address these issues, this paper proposes the MulTKT model, which integrates response intervals, response durations, and the synergistic effects of multiple knowledge concepts, while incorporating a temporal decay factor into the self-attention mechanism to precisely capture learning dynamics. Experimental results indicate that MulTKT more accurately represents knowledge states and predicts student performance.