Identifying Learning Preferences and Strategies in Health Data Science Courses: Systematic Review

Background: Learning and teaching interdisciplinary health data science (HDS) is highly challenging, and despite the growinginterest in HDS education, little is known about the learning experiences and preferences of HDS students.Objective: We conducted a systematic review to identify learning preferences and strategies in the HDS discipline.Methods: We searched 10 bibliographic databases (PubMed, ACM Digital Library, Web of Science, Cochrane Library, WileyOnline Library, ScienceDirect, SpringerLink, EBSCOhost, ERIC, and IEEE Xplore) from the date of inception until June 2023.We followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines and includedprimary studies written in English that investigated the learning preferences or strategies of students in HDS-related disciplines,such as bioinformatics, at any academic level. Risk of bias was independently assessed by 2 screeners using the Mixed MethodsAppraisal Tool, and we used narrative data synthesis to present the study results.Results: After abstract screening and full-text reviewing of the 849 papers retrieved from the databases, 8 (0.9%) studies,published between 2009 and 2021, were selected for narrative synthesis. The majority of these papers (7/8, 88%) investigatedlearning preferences, while only 1 (12%) paper studied learning strategies in HDS courses. The systematic review revealed thatmost HDS learners prefer visual presentations as their primary learning input. In terms of learning process and organization, theymostly tend to follow logical, linear, and sequential steps. Moreover, they focus more on abstract information, rather than detailedand concrete information. Regarding collaboration, HDS students sometimes prefer teamwork, and sometimes they prefer towork alone.Conclusions: The studies'quality, assessed using the Mixed Methods Appraisal Tool, ranged between 73% and 100%, indicatingexcellent quality overall. However, the number of studies in this area is small, and the results of all studies are based on self-reporteddata. Therefore, more research needs to be conducted to provide insight into HDS education. We provide some suggestions, suchas using learning analytics and educational data mining methods, for conducting future research to address gaps in the literature.We also discuss implications for HDS educators, and we make recommendations for HDS course design; for example, werecommend including visual materials, such as diagrams and videos, and offering step-by-step instructions for students.