In the realm of Intelligent Tutoring Systems (ITS), knowledge tracing plays a vital role in capturing students’ evolving knowledge states to predict their future performance. Although significant strides have been made in deep learning-based knowledge tracing research, current methods often fall short of adequately considering students’ global knowledge state representation and modeling local knowledge state representation across different time spans. To address this issue, we introduce a method, Knowledge Tracing Method Based on Enhanced Global and Local Knowledge State Representation (EGLKT), a model that thoroughly incorporates global knowledge state representation and introduces a dynamic multi-step radiation-based local feature extraction method to model local knowledge state representation comprehensively. Our experiments on four public datasets show that EGLKT surpasses comparative models in terms of AUC, affirming its efficacy and potential.