The quality of classroom interaction is an important factor in the effectiveness of language lessons, but managing and sustaining interaction in large, diverse classrooms is a complex and dynamic process. This means that the process is constantly changing and is impacted by multiple internal and external components. A computational method that can possibly be used to investigate these complex dynamic systems is social network analysis. The application of social network models to classroom data allows us to quantify how students and teachers interact in real-time and unravel the drivers behind this behavior. The first study in this project compared the assumptions and principles of complex dynamic systems theory as a metatheory and social network analysis as a toolkit and investigated how a specific social network model (i.e., the Relational Event Model (REM)) can be applied to classroom data in order to better explore this complex dynamic system. In this study, we generated synthetic network data to test the use of this model in an educational context, in preparation of real-life data-analysis that will be conducted in the following studies. The REM can provide accurate estimations of how specific drivers (e.g., timing, inertia, reciprocity, and gender) might shape which, and when interactions occur. In turn, it can inform us on the evolution of the system over time.