Interactive problem solving environments, such as intelligent tutoring systems and educational video games, produce large amounts of transactional data which make it a challenge for both researchers and educators to understand how students work within the environment. Researchers have modeled the student-tutor interactions using complex network representations to automatically derive next-step hints, derive high-level approaches, and create visualizations of student behavior. However, students do not explore the complete problem space. The nonuniform exploration of the problem results in smaller networks and less next-step hints. In this work we explore the possibility of using frequency estimation to uncover locations in the network with differing amounts of student-saturation. Identification of these regions can be used to locate specific problem approaches and strategies that would be most improved by additional student-data. [For complete proceedings, see ED560503.]