A novel framework for deep knowledge tracing via a dual-state joint interaction mechanism

Although deep learning-based knowledge tracing (DLKT) models have shown promising results, they typically attribute student performance solely to knowledge states, neglecting the influence of students’ test-taking psychological states. Moreover, the complex interactions between knowledge states and test-taking psychological states remain underexplored, limiting the potential for further advances in these models. To address this, we propose a novel framework, termed the Dual-state Joint Interaction Mechanism for deep Knowledge Tracing (DJIM-KT), which models the interactions between students’ knowledge states and test-taking psychological states, with the aim of further enhancing the performance of existing DLKT models. In DJIM-KT, DLKT models are first employed to model students’ knowledge states by extracting interaction information between students and exercises. Simultaneously, guided by behaviorist theory, students’ test-taking psychological states are modeled by capturing higher-order relations between exercises and their answering behaviors. Subsequently, we design the dual-state joint interaction mechanism (DJIM), which precisely quantifies the interactions between knowledge states and test-taking psychological states, and leverages reinforcement learning to analyze students’ real-time feedback in different exercises, thereby dynamically adjusting the prediction weights of the two states. This adaptive DJIM enables DJIM-KT to effectively capture individualized student information. Extensive experiments on three real-world datasets demonstrate that DJIM-KT significantly enhances the prediction accuracy and explainability of DLKT models. Specifically, the two representative DLKT models, deep knowledge tracing (DKT) and separated self-attentive neural knowledge tracing (SAINT), achieve average improvements of 17.46% in AUC and 10.37% in ACC with the help of DJIM-KT.