在程序评估中利用叙事反:自动化文本分析的潜力,以支持程序评估中的辅导和决策
Balakrishnan R Nair1, Joyce M W Moonen-van Loon2, Marion van Lierop3
1University of Newcastle, Centre for Medical Professional Development, Newcastle, Australia.
Advances in medical education and practice
|July 25, 2024
概括
对叙事反的自动化文本分析改善了医疗学员和教育工作者的解释. 这种方法通过构建反主题和情绪来增强学习,指导和评估.
科学领域:
- 医疗教育 医学教育
- 计算语言学 计算语言学
- 心理测量 心理测量 心理测量
背景情况:
- 叙事反对于学习,指导和评估中的决策至关重要.
- 解释叙事反可能是具有挑战性和耗时的,阻碍了评估数据的有效使用.
- 需要支持学习者,教练和决策者有效地使用和解释叙事反.
研究的目的:
- 探索自动化文本分析对解释叙事评估数据的有用性.
- 在叙事评估中识别主导反主题和情绪极性.
- 检查反极性,绩效分数和任务判断之间的关联.
主要方法:
- 应用了主题建模和情绪分析,对80名学员的926项临床评估进行了评估.
- 利用自动化技术来识别反主题 (医疗技能,知识,沟通和专业性) 和情绪极性.
- 检查了反情绪,数值绩效得分和整体判断之间的相关性.
主要成果:
- 主题建模确定了三个关键的反主题.
- 评估人员向不符合能力标准的实习生提供了更详细的反.
- 在平均绩效分数和平均情绪极性之间发现了强烈的正相关性,尽管不是在单一评估层面.
结论:
- 自动化文本分析可以带来更高效,结构化和有意义的评估体验.
- 这些技术可以通过帮助解释来促进对评估数据的更深入的对话.
- 进一步的研究对于将自动化文本分析纳入教育实践至关重要,以获得最大的益处.
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