Abstract: Knowledge tracing (KT) aims to dynamically model learners’ evolving knowledge states based on their historical learning records, and plays a significant role in online education systems. Most existing KT methods treat knowledge states as transition patterns of mastery levels of knowledge concepts from completing one exercise to completing the next, and consider learners’ learning records as continuous and uniformly distributed data. However, actual learning records are considered to be divisible into different shorter sessions. To address this, a method, called knowledge concept-aware session modeling for knowledge tracing (KSMKT), is proposed to capture learners’ knowledge state changes at a finer granularity. Specifically, learners’ historical learning records are divided into shorter sessions from the perspective of knowledge concepts. Subsequently, a fine-grained knowledge state modeling module is proposed to capture fine-grained interaction dependencies and knowledge state changes within and across sessions. Additionally, a global knowledge proficiency modeling module is introduced to model learners’ knowledge states from an overall perspective. Extensive experiments on 3 real-world datasets demonstrate that KSMKT outperforms most current baseline methods, thus proving the effectiveness of KSMKT.

Key words: intelligent education, knowledge tracing, sequence modeling, attention mechanism