Insights into Health Data Science Education: A Qualitative Content Analysis

Abstract

BackgroundEarly career researchers in Health Data Science (HDS) struggle to effectively manage their learning process due to the novel and interdisciplinary nature of this field. To date, there is limited understanding about learning strategies in health data science. Therefore, we aim to uncover learning strategies that early career researchers employ to address their educational challenges, as well as shed light on their preferences regarding HDS teaching approach and course design. MethodIn this study, we conducted a qualitative content analysis through semistructured interviews with ten early career researchers, including individuals pursuing masters, PhD, and postdoctoral research programmes in HDS, across two higher education institutions in the United Kingdom. Interviews were carried out in person from June 2023 to August 2023. Data were analysed qualitatively using NVivo software. Descriptive statistics were employed for quantitative analysis. ResultsRegarding learning strategies, we identified ten main categories with 22 codes, including collaboration, information seeking, active learning, focus granularity, elaboration, organisation, order granularity, goal orientation, reviewing, and deep learning strategies. Regarding course design and teaching, we discovered four categories with 14 codes, including course materials, duration and complexity, online discussion, and teaching approaches. ConclusionsEarly career researchers used a range of learning strategies aligned with well-established learning theories, such as peer learning, information seeking, and active learning. It is also evident that learners in HDS favour interactive courses that provide them with hands-on experience and interactive discussion. The insights derived from our findings can enhance the quality of education in HDS.