Footnote:
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Description:
Evaluation methods are necessary and useful for the research and development of digital games. Despite this importance, there is no single best practice approach to detect how players experience games. Many existing approaches suffer from shortcomings such as low temporal resolution, a reliance on sensors, or an unwanted influence on players, e.g., with questionnaires interrupting an experience. In this thesis, we propose and validate methods of player state assessment that are explicitly designed for the application in natural gaming settings such as at-home playing. For that purpose, we define player state in this context and requirements that make assessment methods usable in a natural gaming scenario. This thesis then contributes to human-computer interaction (HCI) and games research by proposing and validating objective (see Part ii) and subjective (see Part iii) assessment methods that can be used to assess player state while adhering to the defined requirements. Further, as these methods are devised as an online assessment, they allow directly reacting to states that are not part of the player experience intended by the developers, such as boredom. We leverage these possibilities by exploring adaptivity based on player state. In Part iv, we present four case studies that assess aspects of player state and adapt game features accordingly. They show that adapting game features to player state can provide a more enjoyable experience, but negative effects can occur such as a decrease in perceived fairness. These projects contribute to game development and academic research. First, we provide subjective and objective methods that allow the assessment of player state in an unobtrusive way enabling an application in a natural gaming setting. Second, we propose and investigate how to leverage these methods in adaptivity approaches that aim to provide an improved play experience. In empirical studies, we evaluate their effects to provide insights into their effectiveness and how adaptivity affects player ...