Mixed Methods Research in Data Analytics Within Higher Education

The integration of data analytics in higher education underscores the need to examine the methodological approaches guiding this research domain. While applications of data analytics have gained extensive attention, their methodological foundations remain underexplored. This review investigates the prevalence, characteristics, and contributions of mixed methods research in data analytics. Following PRISMA guidelines, 22 empirical studies from five databases across were synthesised. Results show that MMR effectively captures the complexities of pedagogy, assessment, and implementation through epistemological complementarity rather than mere methodological combination. Methodological plurality enhances explanatory depth and produces triangulated insights into multifaceted educational phenomena. The review identifies four principles for effective MMR: parallel design integration, sequential design connectivity, analytical triangulation, and methodological transparency. The study advances scholarship by mapping MMR deployment patterns to strengthen research rigour in this evolving field.