UNVaMP：基于潜在知识动态变分正则化的神经知识追踪
UNVaMP: Neural Knowledge Tracing with Variational Regularization of Latent Knowledge Dynamics
AI总结：
UNVaMP是一种结合学生-题目交互与内部记忆的知识追踪方法，纯神经配置UNVaMP-MLP在多数数据集预测性能最优，混合配置UNVaMP-MIRT兼具可解释性且预测成本低，可应用于教育系统并恢复交互潜在结构。
AI中文摘要：
我们提出了统一神经熟练度变分测量（UNVaMP）架构，这是一种知识追踪方法，它将观测到的学生-题目交互与内部记忆相结合，生成学生知识的演化潜在表示。这些表示支持对未来答题情况的准确预测，同时能明确控制估计学习轨迹的平滑度。UNVaMP可配置为纯神经模型，或通过潜在空间上的可解释测量函数预测答题情况的混合模型。我们在四个数据集中的三个上，证明纯神经配置（UNVaMP-MLP）在对比模型中实现了最强的预测性能；而混合配置（UNVaMP-MIRT，使用1PL MIRT测量函数）仅略落后于UNVaMP-MLP，表明可解释性带来的预测成本较小。除预测准确性外，UNVaMP还提供以下功能：用于在估计学生潜在变量时控制波动性的原则性机制、对学生知识状态估计的不确定性进行量化，以及支持异构学生-题目交互特征的灵活输入规范。此外，混合配置UNVaMP-MIRT可生成可解释的即时学生知识状态估计。我们使用实验数据集表明，辅助输入会引发UNVaMP-MIRT预测行为的结构化变化，这与对答题正确性之外的潜在结构的敏感性一致。进一步通过模拟研究表明，UNVaMP在受控测量条件下能产生表现良好的知识状态估计。总体而言，这些结果表明UNVaMP既适用于实际教育系统，又能从学生-题目交互中恢复潜在结构。
英文摘要：
We introduce the Unified Neural Variational Measurement of Proficiency (UNVaMP) architecture, a knowledge tracing method that integrates observed student-item interactions with internal memory to produce evolving latent representations of student knowledge. These representations support accurate predictions of future responses while enabling explicit control over the smoothness of estimated learning trajectories. UNVaMP can be configured as either a purely neural model or a hybrid model that predicts responses through an interpretable measurement function over the latent space. We show that a pure neural configuration (UNVaMP-MLP) achieves the strongest predictive performance among compared models on three out of four datasets. Meanwhile, a hybrid configuration (UNVaMP-MIRT, using a 1PL MIRT measurement function) lags only slightly behind UNVaMP-MLP, indicating that the predictive cost of interpretability is modest. Beyond predictive accuracy, UNVaMP provides the following: a principled mechanism for controlling volatility when estimating student latent variables, quantification of uncertainty over student knowledge state estimates, and flexible input specification that supports heterogeneous student-item interaction features. In addition, the hybrid UNVaMP-MIRT configuration generates interpretable moment-in-time student knowledge state estimates. Using an experimental dataset, we show that auxiliary inputs induce structured changes in the predictive behavior of UNVaMP-MIRT, consistent with sensitivity to underlying structure beyond response correctness. Furthermore, through a simulation study, we show that UNVaMP yields well-behaved knowledge state estimates under controlled measurement conditions. In total, these results indicate that UNVaMP is both useful for real-world education systems and capable of recovering underlying structure from student-item interactions.
