In this paper we explore existing log files of the VIBOA environmental policy game. Our aim is to identify relevant player behaviours and performance patterns. The VIBOA game is a 50 hours master level serious game that supports inquiry-based learning: students adopt the role of an environmental consultant in the (fictitious) consultancy agency VIBOA, and have to deal with complex, multi-faceted environmental problems in an academic and methodologically sound way. A sample of 118 master students played the game. We used learning analytics to extract relevant data from the logging and find meaningful patterns and relationships. We observed substantial behavioural variability across students. Correlation analysis suggest a behavioural trade that reflects the rate of “switching” between different game objects or activities. We were able to establish a model that uses switching indicators as predictors for the efficiency of learning. Also we found slight evidence that students who display increased switching behaviours need more time to complete the games.  We conclude the paper by critically evaluating our findings, making explicit the limitations of our study and making suggestions for future research that links together learning analytics and serious gaming.