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The Knowledge Tracing (KT) task plays a crucial role in personalizedlearning, and its purpose is to predict student responses based on theirhistorical practice behavior sequence. However, the KT task suffers from datasparsity, which makes it challenging to learn robust representations forstudents with few practice records and increases the risk of model overfitting.Therefore, in this paper, we propose a Cognition-Mode Aware VariationalRepresentation Learning Framework (CMVF) that can be directly applied toexisting KT methods. Our framework uses a probabilistic model to generate adistribution for each student, accounting for uncertainty in those with limitedpractice records, and estimate the student's distribution via variationalinference (VI). In addition, we also introduce a cognition-mode awaremultinomial distribution as prior knowledge that constrains the posteriorstudent distributions learning, so as to ensure that students with similarcognition modes have similar distributions, avoiding overwhelmingpersonalization for students with few practice records. At last, extensiveexperimental results confirm that CMVF can effectively aid existing KT methodsin learning more robust student representations. Our code is available athttps://github.com/zmy-9/CMVF.

Cognition-Mode Aware Variational Representation Learning Framework for Knowledge Tracing