用大模型分析学生写作互动，帮外语教师发现作弊行为
LLM-Driven Learning Analytics Dashboard for Teachers in EFL Writing Education
- 基于大模型分析学生与写作系统交互数据
- 识别出非教学目的的ChatGPT使用行为
- 适合关注AI辅助写作教学的外语教师
本文开发了一款专为英语作为外语（EFL）写作教学设计的教师数据分析仪表板。该仪表板利用大语言模型（LLM），分析学生在集成ChatGPT的作文写作系统中的互动行为。通过自然语言处理（NLP）与人机交互（HCI）相结合，帮助教师监控学生学习过程，识别非教育目的的ChatGPT使用，并调整教学策略以匹配学习目标。研究表明，以人为本的设计能有效提升集成ChatGPT的教学环境中的教师仪表板效能。
原文摘要 · Abstract (English)
This paper presents the development of a dashboard designed specifically for teachers in English as a Foreign Language (EFL) writing education. Leveraging LLMs, the dashboard facilitates the analysis of student interactions with an essay writing system, which integrates ChatGPT for real-time feedback. The dashboard aids teachers in monitoring student behavior, identifying noneducational interaction with ChatGPT, and aligning instructional strategies with learning objectives. By combining insights from NLP and Human-Computer Interaction (HCI), this study demonstrates how a human-centered approach can enhance the effectiveness of teacher dashboards, particularly in ChatGPT-integrated learning.
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