MAML-KT: Addressing Cold Start Problem in Knowledge Tracing for New Students via Few-Shot Model-Agnostic Meta Learning
arXiv:2603.00137v1 Announce Type: new Abstract: Knowledge tracing (KT) models are commonly evaluated by training on early interactions from all students and testing on later responses. While effective for measuring average predictive performance, this evaluation design obscures a cold start scenario that arises in deployment, where models must infer the knowledge state of previously unseen students from only a few initial interactions. Prior studies have shown that under this setting, standard empirically risk-minimized KT models such as DKT, DKVMN and SAKT exhibit substantially lower early accuracy than previously reported. We frame new-student performance prediction as a few-shot learning problem and introduce MAML-KT, a model-agnostic meta learning approach that learns an initialization optimized for rapid adaptation to new students using one or two gradient updates. We evaluate MAML-KT on ASSIST2009, ASSIST2015 and ASSIST2017 using a controlled cold start protocol that trains on a
arXiv:2603.00137v1 Announce Type: new Abstract: Knowledge tracing (KT) models are commonly evaluated by training on early interactions from all students and testing on later responses. While effective for measuring average predictive performance, this evaluation design obscures a cold start scenario that arises in deployment, where models must infer the knowledge state of previously unseen students from only a few initial interactions. Prior studies have shown that under this setting, standard empirically risk-minimized KT models such as DKT, DKVMN and SAKT exhibit substantially lower early accuracy than previously reported. We frame new-student performance prediction as a few-shot learning problem and introduce MAML-KT, a model-agnostic meta learning approach that learns an initialization optimized for rapid adaptation to new students using one or two gradient updates. We evaluate MAML-KT on ASSIST2009, ASSIST2015 and ASSIST2017 using a controlled cold start protocol that trains on a subset of students and tests on held-out learners across early interaction windows (questions 3-10 and 11-15), scaling cohort sizes from 10 to 50 students. Across datasets, MAML-KT achieves higher early accuracy than prior KT models in nearly all cold start conditions, with gains persisting as cohort size increases. On ASSIST2017, we observe a transient drop in early performance that coincides with many students encountering previously unseen skills. Further analysis suggests that these drops coincide with skill novelty rather than model instability, consistent with prior work on skill-level cold start. Overall, optimizing KT models for rapid adaptation reduces early prediction error for new students and provides a clearer lens for interpreting early accuracy fluctuations, distinguishing model limitations from genuine learning and knowledge acquisition dynamics.
Executive Summary
This article presents MAML-KT, a model-agnostic meta learning approach that addresses the cold start problem in knowledge tracing for new students. By framing new-student performance prediction as a few-shot learning problem, MAML-KT learns an initialization optimized for rapid adaptation to new students using one or two gradient updates. The authors evaluate MAML-KT on three datasets using a controlled cold start protocol and demonstrate significant gains in early accuracy compared to prior KT models. The results highlight the importance of optimizing KT models for rapid adaptation and provide a clearer lens for interpreting early accuracy fluctuations. This work has significant implications for the development of more effective KT models and their application in real-world educational settings.
Key Points
- ▸ MAML-KT addresses the cold start problem in knowledge tracing for new students
- ▸ MAML-KT learns an initialization optimized for rapid adaptation to new students
- ▸ MAML-KT outperforms prior KT models in early accuracy on three datasets
Merits
Strength in addressing a critical problem
The article tackles a significant challenge in knowledge tracing, namely the cold start problem, and presents a novel solution that improves early accuracy for new students.
Methodological innovation
The use of model-agnostic meta learning is a significant methodological innovation that enables the development of more effective KT models.
Empirical validation
The authors provide thorough empirical validation of MAML-KT on three datasets, demonstrating its effectiveness in real-world settings.
Demerits
Limited generalizability
The study is limited to three datasets, and it is unclear whether MAML-KT will generalize to other educational settings.
Expert Commentary
This article makes a significant contribution to the field of knowledge tracing by addressing the cold start problem and presenting a novel solution that improves early accuracy for new students. The use of model-agnostic meta learning is a significant methodological innovation that enables the development of more effective KT models. The empirical validation of MAML-KT on three datasets provides strong evidence of its effectiveness in real-world settings. However, the study is limited to three datasets, and it is unclear whether MAML-KT will generalize to other educational settings. Furthermore, the implications of this work go beyond the development of more effective KT models, highlighting the importance of optimizing KT models for rapid adaptation and the need for more effective educational technologies.
Recommendations
- ✓ Future studies should investigate the generalizability of MAML-KT to other educational settings and its potential applications in real-world educational contexts.
- ✓ The development of more effective educational technologies that incorporate MAML-KT and other meta learning approaches should be a priority for education policy and research.