探索LLM的潜力,通过教学模拟来增强教学计划
Bihao Hu1,2, Jiayi Zhu3, Yiying Pei4
1School of Computer Science and Technology, East China Normal University, Shanghai, China.
NPJ science of learning
|February 5, 2025
概括
大型语言模型 (LLM) 现在可以通过模拟课堂互动来改进课程计划来增强教师准备. 这种人工智能辅助的方法提高了教学质量,使计划与人类教育工作者可比.
科学领域:
- 教育中的人工智能
- 教学研究教学研究
- 教育技术的教育技术
背景情况:
- 大型语言模型 (LLM) 越来越多地被讨论,因为它们有可能改变教育.
- 现有的研究主要探讨了学生辅导的LLM,对它们在支持教师的作用进行了有限的调查.
- 制定有效的教学计划对于优质的教学至关重要,特别是在高中数学等科目中.
研究的目的:
- 提出和评估一种使用LLM的新方法,以提高高中数学教学计划的质量.
- 探索LLMs在帮助教师进行课前准备和课程改进方面的潜力.
- 调查LLM生成的教学计划,通过模拟交互来改进,是否可以匹配人类制造的计划.
主要方法:
- 开发了一种方法,指导LLM模拟特定科目 (高中数学) 中的教师与学生的互动.
- 利用由模拟互动产生的LLM生成的教学反思来改进最初的课程计划.
- 进行了人体评估,以比较LLM精制教学计划的质量与原始LLM生成的计划和人体专家计划.
主要成果:
- 提出的方法显著提高了LLMs产生的教学计划的质量.
- 人类评估表明,LLM精炼的教学计划与经验丰富的人类教师创建的高质量计划相美.
- 在数学课程中的各种评估维度和知识模块中观察到改善.
结论:
- 通过模拟教学场景和促进课程计划改进,LLM可以有效地用于增强教学准备.
- 这种方法为课前排练提供了有价值的工具,并为改善教材提供了可操作的见解.
- 这些发现提供了实际证据,支持LLMs在支持教师准备和专业发展方面的更广泛应用.
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