用标签+ChatGPT生成个性化学习反馈，减轻教师负担。
A Study on Educational Data Analysis and Personalized Feedback Report Generation Based on Tags and ChatGPT
- 将学生数据转为标签，通过定制提示词生成反馈。
- 20多位数学老师验证报告可靠，反馈具有建设性。
- 适合智能教学系统或教师辅助工具使用。
本研究提出一种新方法，结合标签标注与ChatGPT语言模型，分析学生学习行为并生成个性化反馈。核心是将复杂学生数据转化为大量标签，再通过定制化提示词解码，输出鼓励性而非打击性的建设性反馈。该方法注重精准输入数据至大模型，并设计增强反馈建设性的提示策略。通过超过20位数学教师的调查验证，生成报告具有可靠性。该方法可无缝集成于智能自适应学习系统，或作为工具显著降低教师工作量，为学生提供及时、准确的个性化学习反馈，基于个体需求给出针对性建议。
原文摘要 · Abstract (English)
This study introduces a novel method that employs tag annotation coupled with the ChatGPT language model to analyze student learning behaviors and generate personalized feedback. Central to this approach is the conversion of complex student data into an extensive set of tags, which are then decoded through tailored prompts to deliver constructive feedback that encourages rather than discourages students. This methodology focuses on accurately feeding student data into large language models and crafting prompts that enhance the constructive nature of feedback. The effectiveness of this approach was validated through surveys conducted with over 20 mathematics teachers, who confirmed the reliability of the generated reports. This method can be seamlessly integrated into intelligent adaptive learning systems or provided as a tool to significantly reduce the workload of teachers, providing accurate and timely feedback to students. By transforming raw educational data into interpretable tags, this method supports the provision of efficient and timely personalized learning feedback that offers constructive suggestions tailored to individual learner needs.
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