Game flux analysis with provenance

Abstract

Winning or losing a game session is the final consequence of a series of decisions and actions made during the game. The analysis and understanding of events, mistakes, and fluxes of a concrete game play may be useful for different reasons: understanding problems related to gameplay, data mining of specific situations, and even understanding educational and learning aspects in serious games. We introduce a novel approach based on provenance concepts in order to model and represent a game flux. We model the game data and map it to provenance to generate a provenance graph for analysis. As an example, we also instantiated our proposed conceptual framework and graph generation in a serious game, allowing developers and designers to identify possible mistakes and failures in gameplay design by analyzing the generated provenance graph from collected gameplay data. © Springer International Publishing 2013.