Information Processing Dynamic Graph For Knowledge Tracing
Xiong Xiao, Shengyingjie Liu, Yue Li, Xiuling He, Jing Fang, Yangyang Li · Information Processing & Management · 2025
Knowledge tracking (KT) aims to predict students’ performances by inferring students’ implicit knowledge mastery. Existing methods have limitations in capturing the implicit knowledge construction process of students, which often relies on the automatic integration of knowledge through information processing rather than explicit knowledge association analysis. To address this issue, we propose an Information Processing Dynamic Graph for Knowledge Tracing (IPDGKT) model based on information processing theory. IPDGKT consists of three main components, namely Short-term memory Activation (SA), Long-term memory Perception (LP), and Memory Application (MA) modules. SA extracts higher-order features of exercises and mines long-term dependencies in exercise sequences and contextual information to capture students’ short-term memory. LP characterizes the associative structure of students’ short-term and long-term memory using a Memory Interaction Graph (MIG) and designs a Memory Processing Network (MPN) to simulate the learning process to capture students’ long-term memory. MA uses students’ long-term memory to predict their future performance. We select three publicly available datasets for our experiments, and the results show that IPDGKT produces better performance predictions than existing methods. We demonstrate the value of simulated information processing for KT tasks through visualization experiments. Our code is available at https://github.com/xxiongGG/IPDGKT-main . • We simulated students’ problem-solving processes during response exercises through the deconstruction of information processing theory. • We proposed a novel DLKT model named IPDGKT. It can fully simulate the process of students from exercise to knowledge absorption. • We conducted extensive experiments on three publicly available datasets, and the results demonstrated the effectiveness of IPDGKT.