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.

翻译：知识追踪（KT）任务在个性化学习中具有重要作用，其目标是根据学生历史练习行为序列预测其答题表现。然而，KT任务面临数据稀疏性问题，这使得为练习记录较少的学生学习鲁棒表示变得困难，并增加了模型过拟合的风险。为此，本文提出一种可直接应用于现有KT方法的认知模式感知变分表示学习框架（CMVF）。该框架采用概率模型为每位学生生成分布，以表征练习记录有限学生的不确定性，并通过变分推理（VI）估计学生分布。此外，我们引入认知模式感知多项分布作为先验知识，约束后验学生分布的学习，从而确保具有相似认知模式的学生获得相近的分布，避免因过度个性化导致练习记录较少学生表示偏差。最后，大量实验结果证实，CMVF能有效辅助现有KT方法学习更鲁棒的学生表示。我们的代码开源于 https://github.com/zmy-9/CMVF。