动机,包容性和现实主义应该推动数据科学教育
Candace Savonen1, Carrie Wright1, Ava Hoffman1
1Fred Hutchinson Cancer Center, Seattle, WA, 98109, USA.
数据科学教育可以让各种社区更容易获得,促进创新. 教师可以通过在数据科学课程中关注动机,包容性和现实主义来改善教学.
科学领域:
- 数据科学教育数据科学教育
- 在STEM外展活动中.
- 教学教育学 在教学上
背景情况:
- 数据科学提供了重大机遇,但在许多社区面临着可访问性挑战.
- 扩大数据科学获取对个人有利,并增强该领域的创新和影响.
- 许多数据科学教育工作者缺乏正式的教学培训.
研究的目的:
- 概述一个以动机,包容性和现实主义为中心的数据科学教学哲学.
- 为数据科学课堂提供实施这些教育理想的实际策略.
- 解决数据科学教育中改善可访问性的需要.
主要方法:
- 总结基于与不同受众的小组体验的教学哲学.
- 开发与核心教育理念一致的实用课堂实施想法.
- 基于有效性的教学方法和课程的代改进.
主要成果:
- 数据科学教育的三个理想框架:动机,包容性和现实主义.
- 为教育工作者增强数据科学学习的实用教学策略.
- 在各种学习群体中展示了这些理想的应用.
结论:
- 采用动机,包容性和现实主义的哲学可以显著改善数据科学教育.
- 这些理想的实际实施提高了可访问性和学习成果.
- 持续改进教育方法对于有效的数据科学培训至关重要.
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