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
this https URL

在个性化学习中，知识追踪任务起着至关重要的作用，它的目的是根据学生的历史学习行为序列来预测他们的回答。然而，由于数据稀疏性，知识追踪任务难以学习到适用于少量学习记录的学生的健壮表示，同时增加了模型过拟合的风险。因此，在本文中，我们提出了一种认知模式感知的变分表示学习框架（CMVF），可以直接应用于现有的知识追踪方法。我们的框架使用概率模型为每个学生生成一个分布，考虑到那些具有有限学习记录的学生的不确定性，并通过变分推断（VI）估计学生的分布。此外，我们还引入了一个认知模式感知的多项分布作为先验知识，约束后验学生分布的学习，以确保具有相似认知模式的学生具有相似的分布，避免对少量学习记录的学生过度个性化。最后，广泛的实验结果证实了 CMVF 可以有效地帮助现有的知识追踪方法学习到更健壮的学生表示。我们的代码可在此网址获取