DBGCN

Abstract

Given the extensive use of online learning in educational settings, Knowledge Tracing (KT) is becoming increasingly essential. KT primarily aims to predict a student's future knowledge acquisition based on their past learning activities, thus enhancing the efficiency of student learning. However, the effective acquisition of dynamic and evolving student representations from their historical records presents a formidable challenge. This paper introduces a Knowledge Tracing methodology predicated on Dynamic Broadth Graph Convolutional Networks (DBGCN). DBGCN leverages the mechanisms of breadth graph convolutional networks to proficiently acquire representations of questions and knowledge points from dynamically constructed topological graphs. It employs student state information as an attention query vector to augment student representations, thereby partially mitigating the challenge of capturing the dynamic shifts in user states. The effectiveness of our proposed DBGCN method has been demonstrated through extensive experimentation.