Model-based tutoring systems are driven by an abstract do- main model and solver that is used for solution validation and student guidance. Such models are robust but costly to produce and are not always adaptive to specific students’ needs. Data-driven methods such as the Hint Factory are comparatively cheaper and can be used to generate indi- vidualized hints without a complete domain model. In this paper we explore the application of data-driven hint analy- sis of the type used in the Hint Factory to existing model- based systems. We present an analysis of two probability tutors Andes and Pyrenees. The former allows for flexible problem-solving while the latter scaffolds students’ solution path. We argue that the state-space analysis can be used to better understand students’ problem-solving strategies and can be used to highlight the impact of different design de- cisions. We also demonstrate the potential for data-driven hint generation across systems.