通过人工智能提供对健康数据科学教育的见解
Narjes Rohani1, Kobi Gal2,3, Michael Gallagher4
1Usher Institute, University of Edinburgh, Edinburgh, UK.
BMC medical education
|May 23, 2024
概括
对健康数据科学 (HDS) 学生的人工智能分析揭示了四种学习策略和三种参与策略. 成功的学生利用实际应用,概念连接和同行学习来改善结果.
科学领域:
- 卫生数据科学 (HDS)
- 教育技术的教育技术
- 教育中的人工智能
背景情况:
- 健康数据科学 (HDS) 是一个新兴的跨学科领域,结合了生物,临床和计算科学.
- 医疗保健专业人员在健康和数据科学方面都需要技能.
- 在HDS中分析学生的学习经验对于提高课程设计和教学策略至关重要.
研究的目的:
- 应用人工智能 (AI) 技术来理解学生在健康数据科学 (HDS) 大规模开放在线课程 (MOOC) 中的学习策略和策略.
- 确定有效的学习行为,并提供数据驱动的见解,以改善高质量教育.
- 为课程设计者,教师和学习者提供教学建议.
主要方法:
- 利用人工智能方法分析了HDS MOOC中3000多名学生的学习策略和策略.
- 采用统计测试来检查学生与各种学习资源 (例如,阅读,视频) 的参与以及HDS主题.
- 根据参与程度,将学生的学习策略分为表面,战略和深度学习者.
主要成果:
- 确定了四种关键的学习策略:将新信息与先前的知识联系起来,使用评估来评估,协作学习和重复来记忆.
- 发现了三种不同的学习策略:表面 (低参与度),战略 (中等参与度) 和深度 (高参与度) 的学习者.
- 发现成功的学生优先考虑实用主题,项目工作,讨论,概念联系和同行学习.
结论:
- 人工智能技术为健康数据科学 (HDS) 教育提供了宝贵的见解.
- 从人工智能分析中获得的教学建议可以增强课程设计和HDS学生的学习体验.
- 研究结果支持整合人工智能驱动的分析,以优化像HDS这样的专业领域的教育战略.
关键词:
人工智能的人工智能是人工智能.教育数据挖掘教育数据挖掘健康数据科学健康数据科学医疗信息学 医疗信息学学习分析学习分析.学习参与度学习参与度学习参与度学习战略学习策略学习战术的学习策略医学教育 医学教育更多相关视频
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