Online education has broadened the avenues of research on student s behavior and performance. In this thesis, we shed light on how to support instructors in analyzing student logs from Virtual Learning Environments. Firstly, we conducted interviews with instructors and a systematic mapping of the state-of-art about Education Data Mining and Learning Analytics. Then, we analyzed logs from online courses offered in Brazil and compared our findings with results presented in the literature. Moreover, we gathered instructors preferences in regard to visualization of both students behavior and performance. However, we noted a lack of work showing models to support the development of learning analytics tools. In order to bridge this gap, this thesis presents a model connecting both Visual Analytics theories and models as well as instructors requirements, their visualization preferences, literature guidelines and methods for analyzing student logs. We instantiated and evaluated this model in a tool to assemble dashboards. We captured evidence of their acceptance of our proposal and obtained instructors feedback about the tool such as their both analysis and visualization preferences. Finally, we present some considerations and discuss gaps in existing works that can ground and guide future research, such as new instances of our model, as well as deploying them at Brazilian institutions and evaluating whether there are changes in students performance when instructors are able to see information about their behavior and performance, and act accordingly. It is worth highlighting that the majority of studies presented in this thesis were conducted before the COVID-19 pandemic. Only the last study was performed in the beginning of the pandemic in Brazil.