The potential of data analytics in higher education is well acknowledged. Yet, there is a notable gap in the literature regarding the practical application of theoretical frameworks for its implementation and evaluation. While research has investigated data analytics for personalised learning, student success prediction, and programme assessment, a comprehensive understanding of how these applications align with existing theoretical frameworks is still lacking. This scoping review investigates how theoretical frameworks have been operationalised and applied within empirical data analytics studies. Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) checklist and guidelines, this review searched nine databases and five journals to yield 41 relevant publications used in this review. This scoping review revealed that several studies inadvertently yielded implicit theoretical insights throughout the research process. However, these insights were often not explicitly acknowledged, systematically explored or intentionally incorporated into the study's design. Consequently, this oversight hindered the development of a comprehensive and robust theoretical framework to underpin data analytics research in higher education. Nevertheless, the identified diversity of theoretical frameworks employed in the reviewed studies suggests the potential for a more theoretically grounded approach to analysing complex educational phenomena. To advance data analytics research in higher education, researchers should actively integrate established theoretical frameworks to foster a deeper understanding of data and inform targeted interventions. Future research directions include exploring the strengths and limitations of specific frameworks to guide researchers in selecting appropriate theoretical lenses.