弥合差距:使用自动化文本分析来支持叙述反的解释
Joyce M W Moonen-Van Loon1,2, Marjan Govaerts1, Marion Van Lierop2
1School of Health Professions Education, Maastricht University, Maastricht, the Netherlands.
概括
一个新的工具可视化了医学学生和导师的叙事反,帮助能力发展和学习目标设定. 这一创新增强了程序评估设置中的反解释.
科学领域:
- 医学教育 医学教育
- 程序性评估的程序性评估
- 教育技术的教育技术
背景情况:
- 在程序评估中解释大量的叙事反对学生,导师和评估人员来说是个挑战.
- 需要提供个性化,实时的反解释,与学习成果保持一致的工具.
研究的目的:
- 在医学教育中引入和评估一种用于解释叙事反的创新工具.
- 评估该工具在提高对能力发展和学习目标设定反的使用方面的实用性.
主要方法:
- 与利益相关者进行合作设计会议.
- 使用主题建模和情绪分析开发和试点使用一个工具.
- 将视觉反数据映射到马斯特里赫特大学医学士课程中的预定义能力和专业活动中.
主要成果:
- 该工具在医学士课程中与33名学生和20名导师进行了试点测试.
- 学生和导师都认识到该工具有潜力改善叙事反的使用.
- 学生使用该工具进行自我评估和目标设定;导师使用它进行监测和咨询.
结论:
- 该工具有效地可视化反数据,识别优势,改进领域和学习差距.
- 该工具的可适应性结构允许集成到各种教育框架和电子投资组合中.
- 这种可扩展的创新提供了一个强大的解决方案,可以在全球范围内在基于能力的评估框架中增强叙事反的解释.
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