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通过引导提升医学教育的考试问题质量
1Department of Foundational Medical Studies, Oakland University William Beaumont School of Medicine, Oakland University, Rochester, Michigan, USA.
Anatomical sciences education
|October 18, 2024
概括
医学院可以使用启动程序来使用小数据集可靠地评估考试问题和课程. 这种先进的定量方法提高了医学教育中的评估有效性和效率.
科学领域:
- 医学教育 医学教育
- 量化心理学 量化心理学
背景情况:
- 由于样本规模较小,医学院在课程和考试评估方面面临着挑战.
- 评估教育计划的传统方法需要广泛的数据收集,这是低效的.
研究的目的:
- 证明先进的定量方法的实用性,特别是启动,用于课程和评估评估.
- 为医疗教育提供一种更有效,更可靠的心理测量分析方法.
主要方法:
- 使用引导,一个重新采样技术,从单个数据集生成多个模拟样本.
- 应用这些模拟样本来创建用于评估考试问题和课程的信心区间.
主要成果:
- 引导提供可靠的数据进行评估评估,即使初始样本规模很小.
- 这种方法可以创建置信区间,提高评估的准确性.
结论:
- 引导提供了一个强大的和经济的替代传统的心理测量评估方法.
- 这种技术提高了考试问题的心理测量质量,并为医学教育中的公平,有效的评估做出了贡献.
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