用大模型理解题目文本，更准预测学生答题表现
Next Token Knowledge Tracing: Exploiting Pretrained LLM Representations to Decode Student Behaviour
- 将知识追踪转为文本续写任务，融合题目内容与学习历史
- 在冷启动场景下表现显著优于现有模型
- 适合需要精准个性化推荐的教育AI系统
在教育人工智能中，建模学生知识是关键挑战，直接影响个性化学习效果。知识追踪（KT）旨在根据学生过往交互，预测其对教育题目的回答表现。现有模型多依赖答题正确性及技能标签、时间戳等元数据，常忽略题目文本这一重要教学信息，造成预测性能受限。本文提出下一代令牌知识追踪（NTKT），将KT重构为基于预训练大语言模型（LLM）的下一个词预测任务。NTKT将学生学习历史与题目内容统一表示为文本序列，使LLM同时学习行为模式与语言特征。实验表明，该方法显著优于当前最优神经型KT模型，并在冷启动题目和用户上展现出更强泛化能力。结果凸显了题目文本在知识追踪中的价值，证明利用预训练语言模型表征可更有效地建模学生学习过程。
原文摘要 · Abstract (English)
Modelling student knowledge is a key challenge when leveraging AI in education, with major implications for personalised learning. The Knowledge Tracing (KT) task aims to predict how students will respond to educational questions in learning environments, based on their prior interactions. Existing KT models typically use response correctness along with metadata like skill tags and timestamps, often overlooking the question text, which is an important source of pedagogical insight. This omission poses a lost opportunity while limiting predictive performance. We propose Next Token Knowledge Tracing (NTKT), a novel approach that reframes KT as a next-token prediction task using pretrained Large Language Models (LLMs). NTKT represents both student histories and question content as sequences of text, allowing LLMs to learn patterns in both behaviour and language. Our series of experiments significantly improves performance over state-of-the-art neural KT models and generalises much better to cold-start questions and users. These findings highlight the importance of question content in KT and demonstrate the benefits of leveraging pretrained representations of LLMs to model student learning more effectively.
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