让大模型读懂学生写作过程，反馈更贴心。
"I Wrote, I Paused, I Rewrote" Teaching LLMs to Read Between the Lines of Student Writing
- 用键盘记录和快照捕捉写作过程，让模型了解修改轨迹。
- 学生更喜欢过程感知的反馈，认为其更贴近真实思考。
- 新增内容或重组段落等修改行为与文章质量提升相关。
大型语言模型（LLMs）如Gemini正被广泛用于支持学生写作，但现有反馈仅基于最终作文，缺乏对写作过程的上下文理解。本文探索通过键入日志和周期性快照收集的写作过程数据，能否帮助LLM提供更贴合学习者思维与修订过程的反馈。我们开发了一款数字写作工具，记录学生输入内容及文章演化过程。20名学生使用该工具完成限时作文，反馈分为两类：(i) LLM结合最终作文与完整写作轨迹生成反馈；(ii) 任务后学生填写问卷评估反馈的实用性与共鸣度。初步结果显示，学习者更偏好过程感知型反馈，认为其更契合自身思考。同时发现，新增内容或重组段落等特定修改行为与连贯性、详尽性等评分维度显著相关。研究表明，增强LLM对写作过程的认知，可带来更深入、个性化且更具支持性的反馈。
原文摘要 · Abstract (English)
Large language models(LLMs) like Gemini are becoming common tools for supporting student writing. But most of their feedback is based only on the final essay missing important context about how that text was written. In this paper, we explore whether using writing process data, collected through keystroke logging and periodic snapshots, can help LLMs give feedback that better reflects how learners think and revise while writing. We built a digital writing tool that captures both what students type and how their essays evolve over time. Twenty students used this tool to write timed essays, which were then evaluated in two ways: (i) LLM generated feedback using both the final essay and the full writing trace, and (ii) After the task, students completed surveys about how useful and relatable they found the feedback. Early results show that learners preferred the process-aware LLM feedback, finding it more in tune with their own thinking. We also found that certain types of edits, like adding new content or reorganizing paragraphs, aligned closely with higher scores in areas like coherence and elaboration. Our findings suggest that making LLMs more aware of the writing process can lead to feedback that feels more meaningful, personal, and supportive.
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