Cognition-Mode Aware Variational Representation Learning Framework for
  Knowledge Tracing
Moyu Zhang Xinning Zhu Chunhong Zhang Feng Pan Wenchen Qian Hui Zhao
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.