Providing Insights into Health Data Science Education through Artificial Intelligence

Narjes Rohani, Kobi Gal, Michael Gallagher, Areti Manataki
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Abstract

Background: Health Data Science (HDS) is a novel interdisciplinary field that integrates biological, clinical, and computational sciences with the aim of analysing clinical and biological data through the utilisation of computational methods. Training healthcare specialists who are knowledgeable in both health and data sciences is highly required, important, and challenging. Therefore, it is essential to analyse students' learning experiences through artificial intelligence techniques in order to provide both teachers and learners with insights about effective learning strategies and to improve existing HDS course designs. Methods: We applied artificial intelligence methods to uncover learning tactics and strategies employed by students in an HDS massive open online course with over 3,000 students enrolled. We also used statistical tests to explore students' engagement with different resources (such as reading materials and lecture videos) and their level of engagement with various HDS topics. Results: We found that students in HDS employed four learning tactics, such as actively connecting new information to their prior knowledge, taking assessments and practising programming to evaluate their understanding, collaborating with their classmates, and repeating information to memorise. Based on the employed tactics, we also found three types of learning strategies, including low engagement (Surface learners), moderate engagement (Strategic learners), and high engagement (Deep learners), which are in line with well-known educational theories. The results indicate that successful students allocate more time to practical topics, such as projects and discussions, make connections among concepts, and employ peer learning. Conclusions: We applied artificial intelligence techniques to provide new insights into HDS education. Based on the findings, we provide pedagogical suggestions not only for course designers but also for teachers and learners that have the potential to improve the learning experience of HDS students.
通过人工智能洞察健康数据科学教育
背景:健康数据科学(HDS)是一个新颖的跨学科领域,它整合了生物、临床和计算科学,旨在通过利用计算方法分析临床和生物数据。培养既懂健康科学又懂数据科学的医疗保健专家是非常必要、重要和具有挑战性的。因此,有必要通过人工智能技术分析学生的学习经验,为教师和学习者提供有关有效学习策略的见解,并改进现有的健康与数据科学课程设计。方法:我们应用人工智能方法,在一门有 3,000 多名学生注册的 HDS 大规模开放在线课程中,发现了学生采用的学习策略和战术。我们还使用统计检验来探索学生对不同资源(如阅读材料和讲座视频)的参与情况,以及他们对各种 HDS 主题的参与程度。结果:我们发现,学习人文社科的学生采用了四种学习策略,如主动将新信息与已有知识联系起来、通过评估和编程练习来评估自己的理解程度、与同学合作以及重复记忆信息。根据所采用的策略,我们还发现了三种类型的学习策略,包括低度参与(表层学习者)、中度参与(策略学习者)和高度参与(深度学习者),这与著名的教育理论不谋而合。结果表明,成功的学生会将更多的时间分配给实际课题,如项目和讨论,在概念之间建立联系,并采用同伴学习。结论:我们应用人工智能技术为高分教育提供了新的见解。根据研究结果,我们不仅为课程设计者,还为教师和学习者提供了教学建议,这些建议有可能改善高分专业学生的学习体验。
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