Show simple item record

Files in this item

Thumbnail

Item metadata

dc.contributor.authorRohani, Narjes
dc.contributor.authorGal, Kobi
dc.contributor.authorGallagher, Michael
dc.contributor.authorManataki, Areti
dc.date.accessioned2024-07-09T10:30:12Z
dc.date.available2024-07-09T10:30:12Z
dc.date.issued2024-05-23
dc.identifier302838252
dc.identifierb5d9891a-51eb-4276-915f-958aa5fa187d
dc.identifier85194128627
dc.identifier38783229
dc.identifier.citationRohani , 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/s12909-024-05555-3en
dc.identifier.issn1472-6920
dc.identifier.otherORCID: /0000-0003-3698-8535/work/161228768
dc.identifier.urihttps://hdl.handle.net/10023/30121
dc.descriptionWe 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].en
dc.description.abstractBackground: 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.
dc.format.extent3237973
dc.language.isoeng
dc.relation.ispartofBMC Medical Educationen
dc.subjectArtificial intelligenceen
dc.subjectEducational data miningen
dc.subjectHealth data scienceen
dc.subjectHealth informaticsen
dc.subjectLearning analyticsen
dc.subjectLearning engagementen
dc.subjectLearning strategyen
dc.subjectLearning tacticen
dc.subjectMedical educationen
dc.subjectEducationen
dc.subjectDASen
dc.titleProviding insights into health data science education through artificial intelligenceen
dc.typeJournal articleen
dc.contributor.institutionUniversity of St Andrews. School of Computer Scienceen
dc.identifier.doi10.1186/s12909-024-05555-3
dc.description.statusPeer revieweden


This item appears in the following Collection(s)

Show simple item record