arXiv：cs.LG· Zhangqi Duan, Simon Woodhead, Maya Miller-Vedam, Andrew Lan·· 4 小时前
统一建模问答与辅导对话的IRT框架
A Unified Response Framework for Questions and Tutoring Dialogues
AI 导读
研究者提出一个统一框架，将学生对问题的回答与辅导对话中的子任务映射到共享的项目反应理论（IRT）尺度上。该方法先把辅导对话中的数学子任务改写为独立问题并判定学生作答正确性，再由文本估计题目/子任务难度、追踪学生能力变化，并利用导师在子任务间的引导动作来正则化难度值。在两个含人类与AI辅导对话的真实数学教育数据集上，该框架在题目难度预测和作答正确性预测上均优于强基线。
正文
Abstract:Existing student models in educational applications largely focus on student responses to questions. However, as tutoring becomes more accessible at large scale, it is important to analyze dialogues between tutors and students, which may reveal fine-grained evidence on student knowledge. In this work, we propose a unified framework that models both student responses to questions and tutor-posed subtasks during dialogues on a shared item response theory (IRT) scale. We first identify mathematical subtasks in tutoring dialogues, rewrite them into self-contained questions, and identify the correctness of student responses to these subtasks. We then develop an IRT framework that estimates question/subtask difficulty from text, tracks students' evolving abilities across question-answering and dialogue interactions, and a novel method to leverages tutor moves between consecutive subtasks to regularize their difficulty values. We evaluate our framework on two real-world math education datasets containing both human- and AI-tutored dialogues. Results show that our framework outperforms strong baselines on both question difficulty prediction and response correctness prediction.
| Subjects: | Computation and Language (cs.CL); Computers and Society (cs.CY); Machine Learning (cs.LG) |
| Cite as: | arXiv:2610.19532 [cs.CL] |
| (or arXiv:2610.19532v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2610.19532 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Zhangqi Duan [view email]
[v1]
Wed, 14 Oct 2026 03:47:20 UTC (715 KB)
来源：arXiv：cs.LG · arxiv.org