Democratizing computational skills: evaluating an asynchronous microlearning framework for cloud-based data analytics in health services research

Background Public health is undergoing a digital transformation, with increasing reliance on data-driven decision-making that requires proficiency in computational tools. However, traditional curricula often emphasize theoretical knowledge over applied technical skills, contributing to gaps in workforce readiness. This study evaluated a pilot remote, asynchronous microlearning course designed to expand access to digital skills–specifically R and Google Colab–among students from historically underrepresented backgrounds within the Research Centers in Minority Institutions (RCMI) network. Methods A three-week course, “ Introduction to Cloud Data Analytics for Health Services Research ,” was delivered via a Learning Management System. Students ( N = 19) completed three modules: (1) R and RStudio fundamentals, (2) cloud-based analysis of data from the Health Information for National Trends Survey 7 using Google Colab, and (3) scientific abstract writing. Evaluation included a pre- and post-program five-item objective knowledge assessment, retrospective self-rated competencies (1–5 scale), and a post-program satisfaction survey. Results Mean objective knowledge scores increased significantly from 3.58 to 4.37 ( p = .039). Participants reported statistically significant improvements in eight of nine self-rated competencies ( p < .05), with the largest gains in installing R/RStudio and navigating the interface. Satisfaction was high across domains, particularly for “value for academic development” ( M = 4.5/5.0). Conclusion Brief, asynchronous microlearning experiences can effectively build foundational computational skills and expand access to training for students in low-resourced settings. While technical competencies can be developed within short, flexible formats, more complex skills such as scientific communication may require additional instructional time and support.