CLST: Cold-Start Mitigation in Knowledge Tracing by Aligning a Generative Language Model as a Students’ Knowledge Tracer
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Published
December 5, 2025
Heeseok Jung
Jaesang Yoo
Yohaan Yoon
Yeonju Jang
https://orcid.org/0009-0009-0636-9355
Abstract
Generative large language models (LLMs) are widely utilized in education to assist students to learn and instructors to teach. In addition, generative LLMs are used to support personalized learning by recommending learning content in intelligent tutoring systems (ITSs). Nonetheless, there are few studies utilizing generative LLMs in the field of knowledge tracing (KT), which is a key component of ITSs. KT, which uses students' problem-solving histories to estimate their current levels of knowledge, is regarded as a key technology for personalized learning. Nevertheless, most existing KT models are characterized by their development with an ID-based paradigm, which results in a low performance in cold-start scenarios. These limitations can be mitigated by leveraging the vast quantities of external knowledge possessed by generative LLMs. In this study, we propose cold-start mitigation in knowledge tracing by aligning a generative language model as a students’ knowledge tracer (CLST) as a framework that utilizes a generative LLM as a knowledge tracer. Upon collecting data from math, social studies, and science subjects, we framed the KT task as a natural language processing task, wherein problem-solving data are expressed in natural language, and fine-tuned the generative LLM using the formatted KT dataset. Subsequently, we evaluated the performance of the CLST in situations of data scarcity using various baseline models for comparison. The results indicate that the CLST significantly enhanced performance with a dataset of fewer than 100 students in terms of prediction, reliability, and cross-domain generalization.
How to Cite
CLST: Cold-Start Mitigation in Knowledge Tracing by Aligning a Generative Language Model as a Students’ Knowledge Tracer. (2025). Journal of Educational Data Mining, 17(2), 86-117. https://doi.org/10.5281/
Details
Keywords
intelligent tutoring system, knowledge tracing, personalized learning, large language models applications
References
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Li, L., Zhang, Y., Liu, D., and Chen, L. 2023. Large Language Models for Generative Recommendation: A Survey and Visionary Discussions. arXiv preprint arXiv:2309.01157.
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Malik, A., Wu, M., Vasavada, V., Song, J., Coots, M., Mitchell, J., Goodman, N., and Piech, C. 2019. Generative Grading: Near Human-Level Accuracy for Automated Feedback on Richly Structured Problems. arXiv preprint arXiv:1905.09916.
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Ni, L., Wang, S., Zhang, Z., Li, X., Zheng, X., Denny, P., and Liu, J. 2024. Enhancing Student Performance Prediction on Learnersourced Questions with SGNN-LLM Synergy. In Proceedings of the AAAI Conference on Artificial Intelligence 38, 21, 23232-23240.
Pandey, S., and Karypis, G. 2019. A Self-Attentive Model for Knowledge Tracing. arXiv preprint arXiv:1907.06837.
Pardos, Z.A., and Bhandari, S. 2023. Learning Gain Differences Between ChatGPT and Human Tutor Generated Algebra Hints. arXiv preprint arXiv:2302.06871.
Pardos, Z.A., and Bhandari, S. 2024. ChatGPT-Generated Help Produces Learning Gains Equivalent to Human Tutor-Authored Help on Mathematics Skills. PLoS ONE 19, 5, e0304013.
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