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Mobile learning applications support traditional indoor lectures with outdoor activities using mobile devices. An example scenario is a team of students that use triangulation techniques to learn properties of geometrical figures. In previous work, we developed an agent-based mobile learning application in which students use GPS-enabled phones to calculate distances between them. From practical experience, we learned that the required level of GPS accuracy is not always guaranteed, which undermines the use of the application. In this paper, we explain how we have extended the existing application with a self-adaptation layer, making the system robust to degrading GPS accuracy. The self-adaptive layer is conceived as a set of interacting MAPE loops (Monitor-Analysis-Plan-Execute), distributed over the phones. To guarantee the robustness requirements, we formally specify the self-adaptive behaviors using timed automata, and the required properties using timed computation tree logic. We use the Uppaal tool to model the self-adaptive system and verify the robustness requirements. Finally, we discuss how the formal design supported the implementation of the self-adaptive layer on top of the existing application.