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A video game is an interactive software able to arouse intense emotions in players. Consequentially, different theories have been proposed to understand which game aspects are able to affect the players' emotional state. However, only few works have tried to use empirical evidence to investigate the effects of different game aspects of the players' emotions. In this paper, we present the results of a set of experiments aimed at predicting the players' emotions during video games sessions using their physiological data. We have created a physiological dataset from the data acquired by 33 participants during video game fruition using a standard monitor and a Virtual Reality headset. The dataset contains information about electrocardiogram, 5 facials electromyographies, electrodermal activity, and respiration. Furthermore, we have asked the players to self-assess their emotional state on the Arousal and Valence space. We have then analyzed the contribution of each physiological signal to the overall definition of the players' mental state. Finally, we have applied Machine Learning techniques to predict the emotional state of players during game sessions at a precision of one second. The obtained results can contribute to define game devices and engines able to detect physiological data, as well to improve the game design process.

Benefits of collaborative learning are established and gamification methods have been used to motivate students towards achieving course goals in educational settings. However, different users prefer different game elements and rewarding approaches and static gamification approaches can be inefficient. The authors present an evidence-based method and a case study where interaction analysis and k-means clustering are used to create gamification preference profiles. These profiles can be used to create adaptive gamification approaches for online learning or collaborative learning environments, improving on static gamification designs. Furthermore, the authors discuss possibilities for using our approach in collaborative online learning environments.

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Massively Multi-player Online Role-Playing Games (MMORPGs) and Massively Multiplayer Online games (MMOs) are complex socio-technical distributed systems. In these environments, a huge amount of players interact to have fun and develop their characters. Looting systems have been developed to help allocating valuable in-game objects, gained after finishing quests, as fairly as possible among the participating players. The medium/long term effects of the adoption of different Looting Systems on players have not yet been adequately investigated, in spite of the fact that they could impact heavily on players' satisfaction. In the present work, we move a first step in this direction in order to offer several hints for improvement to game designers and companies developing and managing MMORPGs and MMOs.