RECORD, REPLAY & REFLECT - A FRAMEWORK FOR UNDERSTANDING (SERIOUS) GAME PLAY

In this paper we set the first steps towards defining a framework for understanding game play based on recording user actions allowing for later replay, which we may characterize as symbolic machinima. After discussing issues of user tracking in virtual environments, we introduce a behavioral model for game play, discuss its relation to existing game reference models, and define a metrics to characterize behavioral discreprancy, for example from a norm scenario in a management game. Finally, we will indicate the possible benefits of our approach for understanding individual users' game play, as well as for the re-use of analysis data for authoring alternative game scenarios.