Knowledge tracing (KT) is a core component of intelligent education systems, which aims to predict students’ future performance from their historical learning behaviors. Despite the remarkable progress brought by deep learning, existing models often struggle to effectively capture the dynamic evolution of students’ knowledge states, particularly the intertwined processes of knowledge-acquisition and -forgetting. To address this challenge, we propose the D ynamic K nowledge P erception for Temporal K nowledge T racing (DKPKT) model. DKPKT integrates two complementary modules: the interaction perception module employs a dynamic attention mechanism to extract key interaction features that signal shifts in knowledge states, and the knowledge perception module refines these representations by incorporating knowledge-forgetting and knowledge-acquisition factors, which enable more accurate modeling of knowledge dynamics over time. We evaluate DKPKT against several mainstream baseline models on three real-world educational datasets. Experimental results demonstrate that DKPKT achieves superior predictive performance, validating its effectiveness in modeling knowledge states and enhancing the accuracy of student performance prediction.
Zhu et al. (Wed,) studied this question.
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