Cognition-Mode Aware Variational Representation Learning Framework for Knowledge Tracing
This addresses data sparsity for personalized learning in education, but it is incremental as it builds upon existing KT methods.
The paper tackles data sparsity in Knowledge Tracing (KT) by proposing a Cognition-Mode Aware Variational Representation Learning Framework (CMVF) that uses a probabilistic model with variational inference and a cognition-mode aware prior to generate robust student distributions, and experimental results confirm it effectively aids existing KT methods.
The Knowledge Tracing (KT) task plays a crucial role in personalized learning, and its purpose is to predict student responses based on their historical practice behavior sequence. However, the KT task suffers from data sparsity, which makes it challenging to learn robust representations for students with few practice records and increases the risk of model overfitting. Therefore, in this paper, we propose a Cognition-Mode Aware Variational Representation Learning Framework (CMVF) that can be directly applied to existing KT methods. Our framework uses a probabilistic model to generate a distribution for each student, accounting for uncertainty in those with limited practice records, and estimate the student's distribution via variational inference (VI). In addition, we also introduce a cognition-mode aware multinomial distribution as prior knowledge that constrains the posterior student distributions learning, so as to ensure that students with similar cognition modes have similar distributions, avoiding overwhelming personalization for students with few practice records. At last, extensive experimental results confirm that CMVF can effectively aid existing KT methods in learning more robust student representations. Our code is available at https://github.com/zmy-9/CMVF.