Understanding players to enhance their fun: how to extract player data and motivation factors for procedural content generation

This paper uses results from recent literature on player data collection and Human-Computer Interaction (HCI) fundamentals to classify the data collected by gaming systems to identify different types of players and their motivators. Our study proposes to address the lack of standards and ambiguous identification of data and collection techniques, which hinders progress in the Procedural Content Generation field. Our proposed classification may help researchers and game developers build metrics to evaluate users' motivators and player types, fostering the chance to generate game content to optimize performance, fun, and user satisfaction when playing.