在教育环境中利用大型语言模型进行情感分析
Arfan Ahmed1, Sarah Aziz1, Alaa Abd-Alrazaq1
1AI Center for Precision Health, Weill Cornell Medicine-Qatar, Doha, Qatar.
Studies in health technology and informatics
|April 9, 2025
概括
大型语言模型 (LLM) 显示了教育情绪分析的前景. 这些人工智能工具从定性报告中提供了对学生态度和参与度的更深入的见解,改进了评估.
科学领域:
- 教育技术的教育技术
- 人工智能的人工智能
- 自然语言处理自然语言处理.
背景情况:
- 传统的情绪分析方法与细微的定性数据作斗争.
- 了解学生的情绪对于有效的教育干预至关重要.
研究的目的:
- 探索大型语言模型 (LLM) 在教育环境中对情绪分析的有效性.
- 用学生报告的LLM驱动分析来评估学生的情绪状态和对学术表现的态度.
主要方法:
- 使用大型语言模型 (LLM) 对学生报告的定性分析.
- 情感分析用于衡量情绪状态和态度.
- 基于LLM的分析与传统编码方法的比较.
主要成果:
- 学生报告中的文本数据被有效地处理和分析.
- 情绪分析提供了对学生参与度和需要关注的领域的细微见解.
- 法学士的方法提供了比传统方法更详细的了解学生的情绪.
结论:
- 大型语言模型 (LLM) 显示了教育情绪分析的巨大潜力.
- 法学士申请可以提高教育评估的深度和准确性.
- 这种技术可以促进更有针对性和有效的教育干预.
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