Abstract: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.
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2602.06542 [cs.LG] |
| (or arXiv:2602.06542v3 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2602.06542 arXiv-issued DOI via DataCite |
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| Journal reference: | The 27th International Conference on Artificial Intelligence in Education, Jun 2026, Seoul, South Korea |
From: Jill-Jenn Vie [view email] [via CCSD proxy]
[v1]
Fri, 6 Feb 2026 09:49:28 UTC (459 KB)
[v2]
Mon, 30 Mar 2026 11:23:11 UTC (390 KB)
[v3]
Mon, 27 Apr 2026 14:45:41 UTC (365 KB)
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