Live Knowledge Tracing: Real-Time Adaptation using Tabular Foundation Models
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866915960298930176 |
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| author | Lbath, Mounir Parésy, Alexandre Kaddouri, Abdelkayoum Zighem, Abdelrahman Vie, Jill-Jênn |
| author_facet | Lbath, Mounir Parésy, Alexandre Kaddouri, Abdelkayoum Zighem, Abdelrahman Vie, Jill-Jênn |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_06542 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Live Knowledge Tracing: Real-Time Adaptation using Tabular Foundation Models Lbath, Mounir Parésy, Alexandre Kaddouri, Abdelkayoum Zighem, Abdelrahman Vie, Jill-Jênn Machine Learning 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. |
| title | Live Knowledge Tracing: Real-Time Adaptation using Tabular Foundation Models |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2602.06542 |