On the ASSISTments real-world benchmark, the proposed method achieves the best performance across all metrics, significantly outperforming all baselines¶

Claim · All claims
Evidence · 1 study · design r2 · q2
Currency · newest study 2026 · not yet checked against its sources · not reviewed by a person · recheck by 2029: tool and model accuracy changes quickly

Subclaims¶

q2 i? On the real-world dataset (15 skills), the proposed method achieved Acc. 0.769±0.052, ACE 0.626±0.081, and MSE 0.0571±0.0063, significantly outperforming all baselines (Wilcoxon signed-rank test, p < 0.05). → Ryosuke Nagai 2026

Evidence¶

Ryosuke Nagai 2026¶

Ryosuke Nagai, Kyohei Atarashi, Koh Takeuchi, Jill-Jênn Vie, and Hisashi Kashima. (2026). Estimating Learners' Skill Acquisition Without Temporal Information. https://arxiv.org/abs/2606.20611 doi:10.48550/arxiv.2606.20611

q2 · i? · design · r2

Real-world evaluation on ASSISTments 2009–2010: each learner's log split into an early segment and full log, mastery estimated with a DINA model; 95 learners attempting all 15 skills, 100 random 70:30 train/test splits. Table 2 marks all baselines with the dagger for p < 0.05.

"the proposed method achieves the best performance across all metrics on the real-world dataset, outperforming all baseline methods"

Discussion¶

Related Claims¶

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