Improving Knowledge Tracing via Considering Students' Interaction Patterns

Knowledge Tracing (KT), which aims to accurately identify students' evolving mastery of different concepts during their learning process, is a popular task for providing intelligent tutoring in online learning systems. Recent research has leveraged various variants of single-state recurrent neural networks to model the transition of students' knowledge states. However, students' interaction patterns implicit in learning records are overlooked which plays an important role in reflecting students' mental state and learning habits. Additionally, interaction patterns affect an individual's self-efficacy and knowledge acquisition. To fill this gap, we propose the Interaction Pattern-Aware Knowledge Tracing (IPAKT) model that uses two hidden states to model knowledge state and interaction patterns separately. Specifically, we first extract the interaction patterns from two types of interaction responses: hint and time. Subsequently, these interaction patterns are employed to regulate the update of the knowledge state. Extensive experiments on three common datasets demonstrate that our method achieves state-of-the-art performance. We also present the reasonableness of IPAKT by ablation testing. Our codes are available at https://github.com/SummerGua/IPAKT.