Reexamining the Cold-Start Problem in Knowledge Tracing Models and Implications for SafeInsights

Jiayi Zhang, Ryan S. Baker, Debshila Basu Mallick, Cristina Heffernan, Neil Heffernan — cs.HC

This paper replicates and extends prior work on the cold-start problem in knowledge tracing — the challenge of making accurate predictions when a student begins practicing a new skill. Using a more recent ASSISTments dataset (FoundationalASSIST), the study evaluates KT model performance across both practice trajectories and four problem types: fill-in-the-blank, multiple-choice select-one, multiple-choice select-all, and order/sort. Results show that KT model performance varies across both dimensions, with deep-learning-based models maintaining advantages during early practice but with context-dependent consistency. The study also serves as a proof of concept for SafeInsights, a privacy-preserving research infrastructure designed to facilitate reproducible educational data mining research. This work extends Knowledge Tracing IRT findings by demonstrating that problem type — not just skill — moderates model performance, with implications for Student Modeling in adaptive learning systems.

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Citation

Zhang, J., Baker, R. S., Basu Mallick, D., Heffernan, C., & Heffernan, N. (2026). A Case Study Reexamining the Cold-Start Problem in Knowledge Tracing Models and Implications for SafeInsights, an Education Research Infrastructure. arXiv:2606.11004.