Learning preferences and strategies in health data science courses: a systematic review

Abstract

Background: teaching and learning interdisciplinary Health Informatics (HI) courses is challenging, and despite the growing interest in HI education, little is known about the learning experiences and preferences of HI students.

Objective: we conducted a systematic review to identify the learning preferences and strategies in the HI discipline.

Methods: we searched ten bibliographic databases (PubMed, ACM Digital Library, Web of Science, Cochrane Library, Wiley Online Library, ScienceDirect, Springer Link, EBSCOhost, ERIC, and IEEE Xplore) from date of inception until June 2023. We followed the Systematic Reviews and Meta-Analyses (PRISMA) guidelines and included primary studies written in English that investigated the learning preferences or strategies of students in HI-related disciplines, such as bioinformatics, at any academic level. Risk of bias was independently assessed by two screeners using the Mixed Methods Appraisal Tool (MMAT) and our study results were presented through narrative synthesis.

Results: after abstract screening and full-text reviewing of the 861 papers retrieved from the databases, eight studies, published between 2009 and 2021, were selected for narrative synthesis. The majority of these papers investigated learning preferences, while only one paper studied learning strategies in HI. The systematic review revealed that most HI learners prefer visual presentations as their preferred learning input. In terms of learning process and organisation, they mostly tend to follow logical, linear, and sequential steps. Moreover, they focus more on abstract information, rather than detailed and concrete information. Regarding collaboration, HI students sometimes prefer teamwork and sometimes they prefer to work alone.

Discussion: the studies' qualities are between 73% and 100% according to the MMAT assessment, indicating an excellent quality. However, the number of studies in this area is small and results of all the studies are based on selfreported data. Therefore, more research needs to be done to provide insight into HI education. We provide some suggestions, such as using learning analytics and educational data mining methods for conducting future research to address gaps in the literature. We also discuss implications for HI educators, and we make recommendations for HI course design, for example, we recommend including visual materials, such as diagrams and videos, and offering step-by-step instructions for students.

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Identifiers

ORCID for Stephen Sowa: orcid.org/0000-0002-7095-1843

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