Digital educational games have evolved in recent years due to the need to support education and training focused on non-technical skills. Data gathered through interaction with the graphical user interface are explored and exploited to analyze the players' experience. Many researchers have pointed the importance of analysis of players’ in-game behavior, which can help to enhance the learning process, identify learners' strategies, and improve the effectiveness of the serious game. This study is devoted to the analysis of students' behavior in a simulation game called CLONE, which targets work scheduling, situation awareness, and decision-making. The students’ performance and their behavioral strategies are examined based on sequences analysis of players' in-game actions. Moreover, outlier detection is proposed as an instrument for obtaining information that might help better understand students’ behavior. The findings of the study show that such indicators as time spent on planning schedule, time spent on inspecting additional information, and intensity of delegation activity are significantly higher for successful games than for lost ones. The sequences analysis and clustering reveal students' prevailing in-game strategies, which mostly consist of inspection, reading medical records, delegation, and scheduling. Eventually, outlier detection discloses the game sessions with uncertain strategies and unstructured scheduling.