Deep knowledge tracing models have achieved significant breakthroughs in modeling student learning trajectories. However, these architectures require substantial training time and are prone to overfitting on datasets with short sequences. In this paper, we explore a new paradigm for knowledge tracing by leveraging tabular foundation models (TFMs). Unlike traditional methods that require offline training on a fixed training set, our approach performs real-time ''live'' knowledge tracing in an online way via in-context learning. TFMs align testing sequences with relevant training sequences at inference time, therefore skipping the training step entirely. We demonstrate, using several datasets of increasing size, that our method achieves competitive predictive performance with up to 53x speedups on average, in a setting where student interactions are observed progressively over time.

翻译：深度知识追踪模型在建模学生学习轨迹方面取得了显著突破。然而，这些架构需要大量训练时间，且在处理短序列数据集时容易过拟合。本文探索了利用表格基础模型进行知识追踪的新范式。与需要在固定训练集上进行离线训练的传统方法不同，我们的方法通过上下文学习以在线方式实现实时"动态"知识追踪。TFMs在推理阶段将测试序列与相关训练序列对齐，因此完全跳过了训练步骤。我们使用多个规模递增的数据集证明，在逐步观察学生交互的时间序列场景中，该方法在达到竞争性预测性能的同时，平均实现高达53倍的加速效果。