Enhancing the Knowledge Tracing via a Plug-In Guided Diffusion Model

Knowledge tracing (KT) refers to the problem of predicting students' future performance given their past performance. Scrutinizing previous studies, we can summarize a common learn-to-predict paradigm: a KT model first learns the student's latent knowledge states from historical question-solving learning interactions and then directly predicts whether the student could correctly answer new questions. Alongside the paradigm, existing KT models are dedicated to tailoring refinements for improving predictive performance. However, this has led to increasing model complexity and reduced usability. Inspired by the diagnosis process of human teachers, they conduct correctness prediction based on the students' responses, which are further derived from their latent knowledge states. To achieve this, we propose a novel plug-in Guided diffusiOn mODule (GOOD), which reframes the KT problem as a learn-generate-to-predict paradigm. Specifically, we first employ an existing KT backbone to learn the student's evolving latent knowledge states, subsequently feeding these into our GOOD. Next, GOOD employs a person-wise noise scheduling strategy to add noise to the target responses in the diffusion process, thereby exploring the underlying distribution of response space. Then, GOOD designs a flexible transformer-modulated denoising network to generate target responses utilizing the latent knowledge states as conditional guidance in the reverse process. Finally, the generated responses can explicitly reflect the student's performance, thereby facilitating the correctness prediction. Extensive experiments on four datasets have verified the effectiveness of GOOD in boosting existing KT models to achieve state-of-the-art performance, as well as its generalizability as a flexible plugin.