As science education scholars learn more about how people learn, instructors have begun to shift from teaching science as lists of facts and asking students to synthesize ideas into cognitive models or networks. Therefore, the methodologies we use to understand students’ and instructors’ ways of knowing need to capture this complexity. Within education, one methodology that has emerged to capture this complexity is epistemic network analysis (ENA). ENA is a potentially useful tool for understanding connections between people’s ideas and cognitive constructs. Because of its mixed methods approach, ENA is able to provide the depth of qualitative analysis and allow synthesis and comparison across large quantities of data. In this review, we present findings from a scoping literature review of ENA in science education. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 framework, we extracted data from 19 articles. This data consisted of both context-related variables (i.e., disciplinary field) and application-based variables (i.e., theoretical frameworks, research design). Finally, we discuss the findings from this review and their implications for science education.