  Quantitative analysis of Game User eXperience (GUX) is important to the game
industry. Different from the typical questionnaire analysis, this paper focuses
on the computational analysis of GUX. We aim to analyze the relationship
between game and players using the multi-modal data including physiological
data and game process data. We theoretically extend the Flow model from the
classic skill-and-challenge plane by expanding new dimension on motivation,
which is the result of the multi-modal data analysis on affect, and
physiological data. We call this 3D Flow as Motivational Flow, MovFlow.
Meanwhile, we implement a quantitative GUX Analysis System (GUXAS), which can
predict the player's in-game experience state by only using game process data.
It analyzes the correlation among not only in-game state, but the player's
psychological-and-physiological reaction in the entire interactive game-play
process. The experiments demonstrated our MovFlow model efficiently
distinguished the users' in-game experience states from the perspective of GUX.
