动态的取决于答案的多选题和高招生课程中的整体评估分析
Harnejan K Atwal1, Kenjiro W Quides1,2
1College of Biological Science, UC Davis, Davis, California, USA.
Journal of microbiology & biology education
|June 13, 2024
概括
这项研究引入了一个R编程工作流程,用于自动化多选项评估的分级,使教育工作者获得自由.
科学领域:
- 教育技术的教育技术
- 高等教育教学的教学.
- 计算机科学在教育中的应用
背景情况:
- 在四年制公共机构中,学生与教师的比例很高,这给教学带来了重大挑战.
- 多选题的传统评分方法耗时,限制了教师与学生的互动.
研究的目的:
- 开发和演示一种自动化工作流程,用于使用R编程语言对多选择题进行分级.
- 通过自动化评分和让学生有更多时间参与学习来提高教师团队的效率.
- 为详细的评估分析提供一个多功能工具,包括问题表现和人口统计整合.
主要方法:
- 在R编程语言中实现自定义工作流程,用于对多选项评估进行分级.
- 开发功能来处理错误和评分取决于答案的风格多选题的问题.
- 整合学生人口统计数据进行全面的绩效分析.
主要成果:
- 在R工作流允许快速分级和准确调整多选题中的错误.
- 该系统支持对依赖于答案的问题进行分析,通过诸如基本陈述或Bloom的分类层次等因素进行分类.
- 已证明能够整合学生人口统计数据,以全面了解课程绩效.
结论:
- 开发的R工作流显著提高了高等教育中多选项评估分级的效率.
- 这种方法使教师能够识别影响学生表现的特定因素,并相应地调整教学策略.
- 工作流提供了一个动态和多功能工具,用于评估分析,最终促进一个更有效的学习环境.
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