运用学习分析来识别医学院学业绩差的学生的风险
Thai Ling Wong1, David Hope1, Alan Jaap1
1Medical Education, The University of Edinburgh, Edinburgh, GBR.
Cureus
|September 6, 2024
概括
早期学生参与在线学习,通过测试和讨论提交测量,与更好的学术表现相关. 学习分析提供了一种具有成本效益的方法,可以在评估之前识别有风险的医学学生.
科学领域:
- 医学教育 医学教育
- 教育技术的教育技术
- 学习分析学习分析
背景情况:
- 早期识别有风险的学生对于改善学术成绩至关重要.
- 传统的识别方法可能昂贵且耗时.
- 医疗教育工作者需要可访问和经济有效的方法.
研究的目的:
- 调查学生在线课堂参与和学业表现之间的关系.
- 探索学习分析的实用性,以早期识别有风险的医学学生.
主要方法:
- 对235名爱丁堡大学医学学生 (第一年) 的回顾性研究.
- 在测试提交,讨论板帖子,参与度得分和考试成绩上收集的数据.
- 学习分析专注于在总结性评估之前的讨论板参与.
主要成果:
- 在参与度指标 (提交,分数) 和整体考试成绩 (p<0.001) 之间发现了统计学上显著的相关性.
- 更高的参与度和更多的提交与更好的学业成绩有关.
- 总提交和考试成绩之间的中等相关性 (r=0.324).
结论:
- 增加学生的参与度,特别是通过提交,预测更好的评估表现.
- 早期参与是学业成功的可靠指标.
- 学习分析提供了一个廉价的工具,用于在正式评估之前识别表现不佳的学生.
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