Personalization is an upcoming trend in gamification research, with several researchers proposing that gamified systems should take personal characteristics into account. However, creating good gamified designs is effort intensive as it is and tailoring system interactions to each user will only add to this workload. We propose machine learning algorithm -based personalized content selection to address a part of this problem and present a process for creating personalized designs that allows automating a part of the implementation. The process is based on Deterding’s 2015 framework for gameful design, the lens of intrinsic skill atoms, with additional steps for selecting a personalization strategy and algorithm creation. We then demonstrate the process by implementing personalized gamification for a computer-supported collaborative learning environment. For this demonstration, we use the gamification user type hexad for personalization and the heuristics for effective design of gamification for overall design. The result of the applied design process is a context-aware, personalized gamification ruleset for collaborative environments. Lastly, we present a method for translating gamification rulesets to machine-readable classifier algorithm using the CN2 rule inducer.

Different physiological theories and experiments have studied the link between emotions and humans' information provided by biofeedback sensors. However, only few works have been proposed regarding the acquisition of physiological data in order to investigate the emotions of video game players. In this paper, we propose an overview of different features which can be extracted from a set of physiological data acquired from players during video game sessions. Moreover, we provide a method to select only the most important features to use in a generic supervised learning algorithm. With these features, researchers can develop a model able to predict, in real-time, the players' emotions. Thus, we have conducted a set of experiments, in which we have acquired a set of physiological information, and the self-assessed participants' emotional state. On these data, we have applied a feature selection algorithm providing an overview of the most interesting physiological signals and features that should be considered during the studies on emotions in video game research field.