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使用机器学习和人类编码分析药房学生的反,揭示了对教师教学和课程质量的关键见解. 这种数据驱动的方法增强了医疗保健专业教育中的教学设计和学生学习经验.

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教师课程评价学院课程评价机器学习 机器学习药房教育 在药房教育.学生的评论 学生的评论专题分析 专题分析

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科学领域:

  • 药房 教育 教育 药房 教育
  • 健康 专业 教育 卫生 专业 教育
  • 教育技术的教育技术

背景情况:

  • 定性教师和课程评估 (FCE) 反为学生的学习体验提供了宝贵的见解.
  • 分析开放式学生评论对于教学增强和课程开发至关重要.
  • 传统的反分析方法可能无法完全捕捉定性数据中的细微差别.

研究的目的:

  • 使用集成机器学习和人类编码方法调查药学学生的定性FCE反.
  • 发现有关教师教学,课程质量和需要改进的领域的可操作的见解.
  • 在健康专业教育中为教学增强策略提供信息.

主要方法:

  • 在2019-2023年使用文本挖掘软件 (WordStat) 对1267个FCE的分析.
  • 机器学习技术的应用:词汇聚类,并发映射,短语提取和话题建模.
  • 补充手册主题分析使用演和归纳编码,由描述性统计数据支持.

主要成果:

  • 机器学习识别了关键术语 (教授,班级,教学) 和短语 (优秀的教授,知识博的教授).
  • 主题建模揭示了诸如理解材料,伟大的教授和现实生活经验等主题.
  • 手动编码确定了三个主要主题:教师的个人属性 (45.86%),教学效率 (28.92%),和课程质量 (23.24%).

结论:

  • 将机器学习与人类编码相结合,可以有效地分析定性FCE数据,以获得更深入的见解.
  • 学生反分析可以为课程设计和教学效率的数据驱动决策提供信息.
  • 这种方法增强了对健康专业教育中学生学习经验的理解.