Estimating Learners' Skill Acquisition Without Temporal Information

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.

Connected Concepts

Student Modeling

Knowledge Tracing

Adaptive Learning

Personalized Learning

Learning Analytics

Formative Assessment

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Citation

Ryosuke Nagai, Kyohei Atarashi, Koh Takeuchi, Jill-Jênn Vie, Hisashi Kashima (2026). Estimating Learners' Skill Acquisition Without Temporal Information. arXiv:2606.20611. AIED 2026, Seoul