Towards Better Understanding of Player's Game Experience
Abstract
Improving player's game experience has always been the common goal of video game practitioner. In order to get a better understanding of player's perception of game experience, we carry out experimental study for data collection and present game experience prediction model based on machine learning method. The model is trained on the proposed multi-modal database which contains: physiological modality, behavioral modality and meta-information to predict the player game experience in terms of difficulty, immersion and amusement. By investigating the model trained on separate and fusion feature sets, we show that physiological modality is effective. Moreover, better understanding is achieved with further analysis on the most relevant features in the behavioral and meta-information features set. We argue that combining the physiological modalities with behavioral and meta information can provide a better performance on the game experience prediction.
Keywords
CCS CONCEPTS • General and reference → Empirical studies
Experimenta- tion
• Applied computing → Computer games
• Human- centered computing → User models
KEYWORDS User experience research
game experience
affective gaming
phys- iological signal
game events
machine learning
interpretability
CCS CONCEPTS • General and reference → Empirical studies