Ryosuke Nagai, Kyohei Atarashi, Koh Takeuchi, Jill-Jênn Vie, Hisashi Kashima (2026) — AIED 2026, Seoul 📄 Full text (arXiv)
Nagai et al. (2026) tackle the practical problem that many real-world educational datasets contain only single-time-point assessments (snapshots) without temporal information, making standard time-series knowledge tracing approaches inapplicable. They propose a novel framework that uses inclusion relations among learners' skill sets — interpreting expanding skill sets as a proxy for learning progression — to induce a pseudo-temporal ordering from snapshot data. A neural model captures latent skill acquisition dynamics through expected skill increments. Experiments on both synthetic and real-world datasets show consistent outperformance over baselines, with particularly strong advantages as the skill space grows. This work bridges student-modeling and knowledge-tracing for data-constrained environments, enabling adaptive-learning support and personalized-learning in settings where longitudinal data is unavailable — a significant practical advance for learning-analytics and formative-assessment in low-resource contexts.
Related Pages
- skill-acquisition-without-temporal-info -- This page