人工智能辅助的定性分析形成性反,以支持以学生为中心的生理学学习
Sulekha Anand1, Ursula Holzmann1, Alexander Y Payumo1
1Department of Biological Sciences, San Jose State University, San Jose, CA 95192, USA.
Advances in physiology education
|December 26, 2025
概括
大型语言模型 (LLM) 有效地分析学生的反,识别生理学讲座中常见的困惑点. 这支持以学生为中心的学习,使实时的教学调整.
科学领域:
- 教育技术的教育技术
- 教育中的人工智能
- 高等教育教学的教学.
背景情况:
- 大型课程对教师在及时分析学生反方面提出了挑战.
- 以学生为中心的学习 (SCL) 需要有效地综合学生的投入,以适应教学.
- 形成性反对于识别学习差距和告知教学策略至关重要.
研究的目的:
- 评估大型语言模型 (LLM) 在分析以学生为中心的学习 (SCL) 的形成性反方面的有效性.
- 将LLM分析的速度和主题可靠性与学生反的人类分析进行比较.
- 探索LLMs在支持大量学生投入的教师方面的潜力.
主要方法:
- 利用Claude Sonnet 4.0和ChatGPT 4.1来分析63个匿名学生对"最泥点"提示的回应.
- 进行了20次分析,以评估LLM绩效的一致性和可靠性.
- 将LLM分析时间和主题识别与人类评审员的评估进行比较.
主要成果:
- 这两种LLM都始终确定了一致的LLM.
- 通风和肺部机械学
- "作为学生困惑的主要领域.
- 实际上,LLMs比人类审核员 (32分钟) 完成分析的速度要快得多 (平均为19.6s/31.0s).
- 在LLM运行中观察到主题可靠性,与人类分析保持一致.
结论:
- LLM提供了一种高效的方法来处理形成性反,使教师能够实时调整教学.
- 这项技术通过结合所有学生的声音来支持以学生为中心的学习和教育公平.
- 虽然对形成性评估有希望,但LLM的变化性需要对高风险的总结性评估进行进一步的细化.
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