Model-based testing facilitates automatic generation of test cases by means of models of the system under test. Correctness and completeness of these models determine the effectiveness of the generated test cases. Critical faults can be missed due to omissions in the models, which are primarily created manually. In practice, these faults are usually detected with exploratory testing performed manually by experienced test engineers. In this paper, we propose an approach for refining system models based on the experience and domain knowledge of these test engineers. Our toolset analyzes the execution traces that are recorded during exploratory testing activities and identifies the omissions in system models. The identified omissions guide the refinement of models to be able to generate more effective test cases. We applied our approach in the context of an industrial case study to improve the models for model-based testing of a Digital TV system. After applying our approach, three critical faults were detected. These faults were not detected by the initial set of test cases and they were also missed during the exploratory testing activities.