A game-based corpus for analysing the interplay between game context and player experience

Recognizing players’ affective state while playing video games has been the focus of many recent research studies. In this paper we describe the process that has been followed to build a corpus based on game events and recorded video sessions from human players while playing Super Mario Bros. We present different types of information that have been extracted from game context, player preferences and perception of the game, as well as user features, automatically extracted from video recordings. We run a number of initial experiments to analyse players’ behavior while playing video games as a case study of the possible use of the corpus.peer-reviewe

A structured expert evaluation method for the evaluation of children's computer games

Inspection-based evaluation methods predicting usability problems can be applied for evaluating products without involving users. A new method (named SEEM), inspired by Norman’s theory-of-action model [18] and Malone’s concepts of fun [15], is described for predicting usability and fun problems in children’s computer games. This paper describes a study to assess SEEM’s quality. The results show that the experts in the study predicted about 76% of the problems found in a user test. The validity of SEEM is quite promising. Furthermore, the participating experts were able to apply the inspection-questions in an appropriate manner. Based on this first study ideas for improving the method are presented

Creating an Emotionally Adaptive Game

To optimize a player's experience, an emotionally adaptive game continuously adapts its mechanics to the player's emotional state, measured in terms of emotion-data. This paper presents the first of two studies that aim to realize an emotionally adaptive game. It investigates the relations between game mechanics, a player's emotional state and his/her emotion-data. In an experiment, one game mechanic (speed) was manipulated. Emotional state was self-reported in terms of valence, arousal and boredom-frustration-enjoyment. In addition, a number of (mainly physiology-based) emotion-data features were measured. Correlations were found between the valence/arousal reports and the emotion-data features. In addition, seven emotion-data features were found to distinguish between a boring, frustrating and enjoying game mode. Taken together, these features convey sufficient data to create a first version of an emotionally adaptive game.</p