How do teachers use online student assessment data? School districts invest increasing resources in online systems for reporting and analyzing student assessment data, yet few studies describe the direct use of such applications or explore how these systems relate to teachers’ professional roles, data use attitudes, or data use efficacies. This dissertation applies learning analytics methods for log file analysis and visual data analytics to explore the extensive variation in teachers’ online data use behaviors and attitudes over six months in one urban secondary school. Descriptive statistics and visualizations of online usage over time demonstrate strong connections between teachers’ online behavior and common organizational factors, such as school level (middle vs. high school), content area, and required training. Correlational evidence suggests that data use self-efficacies have stronger relationships to online use than general data use attitudes. Hierarchical cluster analysis heatmaps are used to identify novel subgroups of teacher online data use behaviors and attitudes. These exploratory findings are used to generate data use dashboards for school-based leadership and an expanded determinant framework for the adoption of online assessment systems. Combining data-intensive methods with theoretical frameworks for self-efficacy, technology acceptance, and use diffusion, this dissertation aims to describe the rich variation in teachers’ online data use and attitudes, as well as productively inform the practice and study of data-based decision making in schools.