基于标签与ChatGPT的教育数据分析及个性化反馈报告生成研究
A Study on Educational Data Analysis and Personalized Feedback Report Generation Based on Tags and ChatGPT
AI总结：
本研究提出结合标签标注与ChatGPT的教育数据分析方法，将学生数据转为标签后经定制提示生成激励性个性化反馈，经20余名数学教师验证可靠，可集成到自适应学习系统或作为工具减轻教师负担、提供及时适配的学习反馈。
摘要：
本研究提出了一种结合标签标注与ChatGPT语言模型的新方法，用于分析学生学习行为并生成个性化反馈。该方法的核心是将复杂的学生数据转换为丰富的标签集，再通过定制化提示词对这些标签进行解码，以输出能够激励而非打击学生的建设性反馈。该方法聚焦于将学生数据准确输入大语言模型，并设计可提升反馈建设性的提示词。研究通过对20余名数学教师开展的调研验证了该方法的有效性，教师们证实生成的报告具备可靠性。该方法可无缝集成到智能自适应学习系统中，也可作为工具使用，能大幅减轻教师工作负担，为学生提供准确及时的反馈。通过将原始教育数据转化为可解释的标签，该方法支持提供高效及时的个性化学习反馈，给出契合学习者个体需求的建设性建议。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
