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System-One LLM shows promise for knowledge tracing with limited learner data

By PulseAugur Editorial · [1 sources] ·

A new research paper explores the potential of System-One Large Language Models (LLMs) for knowledge tracing (KT) in educational settings, particularly when limited learner data is available. The study introduces a model called Jev, which, even without specific training data from a new platform, achieved a higher AUC score than existing deep KT models trained on a small number of learners. Further enhancements with JevKT, incorporating limited logged data and similar-learner statistics, improved performance, outperforming deep KT models up to 16 learners. AI

IMPACT This research suggests LLMs could significantly reduce the data requirements for educational modeling, enabling personalized learning from the outset.

RANK_REASON Research paper published on arXiv detailing a new approach to knowledge tracing using LLMs. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

System-One LLM shows promise for knowledge tracing with limited learner data

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Research paper published on arXiv detailing a new approach to knowledge tracing using LLMs. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Unggi Lee, Haeun Park ·

    Can a System-One LLM Perform Knowledge Tracing When Few or No Learners Are Logged?

    arXiv:2610.11135v1 Announce Type: new Abstract: Knowledge tracing (KT) models need many logged learners, so a new course or platform starts without a usable model. In LLM-based KT the LLM generates the answer, which we call System-Two; it is either fine-tuned on the target data o…

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