一个以过程为导向的视角,对服务前教师的自我效能和他们的动机信息:使用大型语言模型来分类教师的演讲
Olivia Metzner1, Yindong Wang2, Wendy Symes1,3,4
1Department of Educational Sciences, University of Potsdam, Potsdam, Germany.
The British journal of educational psychology
|April 29, 2025
概括
教师的自我效能影响动机信息,影响学生的学习. 一个大型语言模型 (LLM) 在分类这些信息方面表现有前途,有助于教育研究.
科学领域:
- 教育心理学教育心理学
- 教育中的人工智能
背景情况:
- 现有的关于教师动机和学生学习的研究仅限于成就背景和调查数据.
- 教师特征,如自我效能,与他们使用动机信息之间的关系需要进一步调查.
研究的目的:
- 用大型语言模型 (LLM) 将教师的演讲分类为基于自决理论 (SDT) 的动机信息.
- 分析教师服务前自我效能 (TSE) 和他们使用激励信息之间的关系.
主要方法:
- 对119名预服务教师课堂录音的人类评分注释被用于微调法学士学位.
- 分析了来自103名服务前教师的数据,包括调查和视频记录,以检查TSE和激励信息的使用情况.
主要成果:
- 精心调整的LLM在评估基于SDT的激励信息方面表现良好,尽管对于挫败信息需要进一步改进.
- 课堂管理的服务前TSE与相关性支持信息正相关.
- 学生参与的TSE减少了对能力或相关性阻碍信息的使用,而对教学策略的TSE减少了对自主性的支持信息.
结论:
- 法律法规可以可靠地评估基于SDT的动机信息,为教育研究提供可扩展的工具.
- 教师的自我效能是影响在课堂上使用激励信息的类型和频率的一个重要因素.
- 研究结果强调了人工智能在理解细微的师生互动和教师培训计划方面的潜力.
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