Providing insights into health data science education through artificial intelligence
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.
Citation
Rohani , N , Gal , K , Gallagher , M & Manataki , A 2024 , ' Providing insights into health data science education through artificial intelligence ' , BMC Medical Education , vol. 24 , no. 1 , 564 . https://doi.org/10.1186/s12909-024-05555-3
Publication
BMC Medical Education
Status
Peer reviewed
ISSN
1472-6920Type
Journal article
Description
We would like to thank the Precision Medicine programme of the University of Edinburgh, as well as the Medical Research Council, for their support of this project aimed at enhancing health data science education. Additionally, we would like to express our appreciation to the Coursera platform and the students who participated in the course, whose contribution was invaluable to this research. This work was supported by the Medical Research Council [grant number MR/N013166/1].Collections
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