在禾中找到针头:学生对教师的评价的自然语言处理可以识别教学问题吗?
C Jessica Dine1, Judy A Shea1, Caitlin B Clancy1
1Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA.
Journal of general internal medicine
|August 21, 2024
概括
自然语言处理 (NLP) 可以从学生对教学 (SET) 评论的评估中识别教学质量问题. 免费的情绪分析工具显示出有前途的表现,与定制编码字典相比.
科学领域:
- 医学教育 医学教育
- 自然语言处理自然语言处理.
- 计算语言学 计算语言学
背景情况:
- 机构使用学生对教学的评估 (SET) 来评估教学质量.
- 手动审查叙事SET评论是耗时和资源密集的,用于识别教师的教学问题.
研究的目的:
- 评估自然语言处理 (NLP) 在分析学生对教学 (SET) 评论的评估中的有效性.
- 为了确定NLP是否可以从学习者反中准确地识别有关教学质量的问题.
主要方法:
- 11,850名学生对教学评价 (SET) 的回顾性队列分析,来自临床轮换的评论 (2017年7月至2018年6月).
- 三个定制开发的NLP字典与一本现成的情感字典进行性能评估的比较.
主要成果:
- 专家词典的准确度为0.90,精度为0.62,回忆率为0.50.
- 情感加上资格词典表现出很好的准确性 (0.86) 和回忆 (0.77),精度为0.37.
- 一个现有的,免费的NLP情绪分析字典的性能与定制编码字典相当.
结论:
- 自然语言处理 (NLP) 方法提供了一种可行的方法,可以在学生对教学 (SET) 的评价中识别教学质量问题.
- 虽然NLP表现出良好的准确性和合理的回忆力,但准确性仍然是一个挑战.
- 免费,易于使用的NLP情绪分析工具可以成为资源密集型定制字典开发的有效替代品.
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