In this paper, we take a preliminary step towards solving the problem of
causal discovery in knowledge tracing, i.e., finding the underlying causal
relationship among different skills from real-world student response data. This
problem is important since it can potentially help us understand the causal
relationship between different skills without extensive A/B testing, which can
potentially help educators to design better curricula according to skill
prerequisite information. Specifically, we propose a conceptual solution, a
novel causal gated recurrent unit (GRU) module in a modified deep knowledge
tracing model, which uses i) a learnable permutation matrix for causal ordering
among skills and ii) an optionally learnable lower-triangular matrix for causal
structure among skills. We also detail how to learn the model parameters in an
end-to-end, differentiable way. Our solution placed among the top entries in
Task 3 of the NeurIPS 2022 Challenge on Causal Insights for Learning Paths in
Education. We detail preliminary experiments as evaluated on the challenge's
public leaderboard since the ground truth causal structure has not been
publicly released, making detailed local evaluation impossible.

本篇研究提出了一种深度知识追踪模型中的新型因果门循环单元（ GRU）模块，使用学习排列矩阵及以可选学习下三角矩阵的方式，解决了技能之间的因果关系问题，该解决方案在教育领域中有着重要的应用价值。