大卫与哥利亚特:比较传统的机器学习和一个大型语言模型来评估学生在物理问题中的概念使用
Fabian Kieser1, Paul Tschisgale2, Sophia Rauh3
1Physics and Physics Education Research, Heidelberg University of Education, Heidelberg, Germany.
Frontiers in artificial intelligence
|October 3, 2024
概括
传统的机器学习算法在评估物理概念使用方面表现优于大型语言模型. 传统方法提供了更多的控制,可以补充大型语言模型,特别是可用的标记数据.
科学领域:
- 教育研究教育研究
- 教育中的人工智能
- 物理教育 物理教育
背景情况:
- 大型语言模型 (LLM) 对教育研究和评估有希望.
- 法律法学具有局限性,包括知识幻觉,缺乏解释性和高资源成本.
- 传统的机器学习 (ML) 提供了更多的研究人员控制,但LLM的最佳用例尚不清楚.
研究的目的:
- 在评估学生物理概念使用时,将传统ML算法的性能与大型语言模型进行比较.
- 确定在教育评估中采用传统的ML与LLMs的最佳情况.
主要方法:
- 一个物理问题解决任务被用来评估学生的概念应用.
- 实施了传统的机器学习算法,并与大型语言模型进行了比较.
- 分析了模型分类,以了解决策过程.
主要成果:
- 结合常规机器学习算法,与大型语言模型相比,表现优越.
- 对模型决策的分析提供了对分类差异的见解.
- 在这种特定的学生评估背景下,传统的ML证明更有效.
结论:
- 传统的机器学习算法可以在特定的教育评估任务中超过大型语言模型.
- 传统的ML可以有效地补充LLM,特别是当有标记数据可用时.
- 传统的ML和LLMs之间的选择取决于具体的研究需求和数据的可用性.
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