<p>Knowledge Tracing (KT) aims to leverage students’ learning interactions to trace their knowledge state and accurately predict future learning performance. We observe that the learning process is carried out in stages, with each stage having different learning intention that drive students’ behavior and performance. In fact, within and across these learning stages, students’ knowledge state may vary due to variations in their learning intention. Most existing KT methods consider students’ learning interactions as a continuous process, overlooking the staged variations in students’ learning intention, leading to inconsistent representation of students’ actual knowledge state. To address this problem, we explore a new paradigm of KT and propose a novel model named Learning Intention-Aware Knowledge Tracing for Learning Stage (ISKT), which perceives the learning intention of staged variations to trace the students’ knowledge state. Specifically, we have designed a hierarchical intention-aware network, which separately mines the interaction relations within learning stages and the stage relations between the learning stages, to perceive learning intention at both the interaction and stage levels. This network also provides an effective representation of intention for the entire learning process by adaptively integrating these dual levels of learning intention. Additionally, to represent the staged knowledge state, we utilize Knowledge gain within learning stages and knowledge forgetting across learning stages to model the staged learning progress. We design an intention fusion method, which learns the fusion coefficient between learning intention and staged learning progress, and then performs fusion based on this coefficient. Extensive experimental results on public datasets demonstrate that ISKT outperforms state-of-the-art baseline models in predicting students’ future performance.</p>