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To study whether psychophysiological indicators are suitable measures of user experience in a digital exercise game (exergame), a laboratory study employing both psychophysiological and self‐report measures was conducted. Sixty‐six participants cycled for 10 min on an ergometer while pupil diameter, skin conductance, and heart rate were measured; afterward, they completed a user experience questionnaire. The participants performed under three experimental conditions varying between subjects: active gaming (participants controlled the altitude of a digital bird by varying their pedal rate in order to catch letters flying across the screen), observing a game (they observed a replay of another participant’s game), and no‐game (blank screen). Only the gaming condition showed evidence for statistically significant pupil dilations—indicating emotional arousal—in response to game events (catching a letter) or corresponding points in time. The observational condition did not differ statistically from the no‐game control condition. Self‐reports also indicated that the gaming condition was rated most fun and least demanding. Other psychophysiological indicators (heart rate, skin conductance) showed no systematic effects in response to game events, rather they steadily increased during training. Thus, pupil responses were shown to be suitable indicators of positive emotional reactions to game events and user experience in a (training) game.

Comprehensive evaluation studies are necessary to “prove” the benefit of Serious Games (SG). This is also extremely important for the commercial success of SG: Best practice examples with profound, well-recorded positive effects will provide relevant arguments to invest into SG for training/education, sports and health, and other application domains. On the other hand, it is not easy to prove the benefit of SG and to measure its effects (e.g. learning effects or medical effects) and affects (user experience factors such as fun during play). Evaluation methodologies might be split into observation, self-evaluation (e.g. questionnaires, interviews), associative methods, performance analyses, and psychophysiology measurement. Technology-enhanced evaluation methods, for instance, facing expression measurement are in the centre of attention. This chapter provides an overview of these methods and describes current interdisciplinary research and technology development achievements in that field.

For adaptation and personalization of game play sophisticated player models and learner models are used in game-based learning environments. Thus, the game flow can be optimized to increase efficiency and effectiveness of gaming and learning in parallel. In the field of gaming still the Bartle model is commonly used due to its simplicity and good mapping to game scenarios, for learning the Learning Style Inventory from Kolb or Index of Learning Styles by Felder and Silverman are well known. For personality traits the NEO-FFI (Big5) model is widely accepted. When designing games, it is always a challenge to assess one player's profile characteristics properly in all three models (player/learner/personality). To reduce the effort and amount of dimensions and questionnaires a player might have to fill out, we proved the hypothesis that both, Learning Style Inventory and Bartle Player Types could be predicted by knowing the personality traits based on NEO-FFI. Thus we investigated the statistical correlations among the models by collecting answers to the questionnaires of Bartle Test, Kolb LSI 3.1 and BFI-K (short version of NEO-FFI). A study was conducted in spring 2012 with six school classes of grade 9 (12-14 year old students) in two different secondary schools in Germany. 74 students participated in the study which was offered optionally after the use of a game-based learning tool for peer learning. We present the results statistics and correlations among the models as well as the interdependencies with the student's level of proficiency and their social connectedness. In conclusion, the evaluation (correlation and regression analyses) proved the independency of the models and the validity of the dimensions. Still, especially for all of the playing style preferences of Bartle's model significant correlations with some of the analyzed other questionnaire items could be found. As no predictions of learning style preferences is possible on the basis of this studies data, the final recommendation for the development of game-based learning application concludes that separate modeling for the adaptation game flow (playing) and learn flow (learning) is still necessary.

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