In the era of digital gaming, there is a pressing need to better understand how people's gaming preferences and habits affect behavior and can inform educational game design. However, instruments available for such endeavor are rather informal and limited, lack proper evaluation, and often yield results that are hard to interpret. In this paper we present the design and preliminary validation (involving N = 754 Spanish secondary school students) of a simple instrument that, based on a 10-item Game Preferences Questionnaire (GPQ), classifies participants into four ‘clusters’ or types of gamers, allowing for easy interpretation of the results. These clusters are: (1) Full gamers, covering individuals that play all kinds of games with a high frequency; (2) Hardcore gamers, playing mostly first-person shooters and sport games; (3) Casual gamers, playing moderately musical, social and thinking games; and (4) Non-gamers, who do not usually play games of any kind. The instrument may have uses in psychology and behavioral sciences, as there is evidence suggesting that attitudes towards gaming affects personal attitudes and behavior. Besides, we propose applying the instrument to help designers of educational games to get better tailored their games to their target audiences

This study examines the influence of players' age, gender, and gaming preferences and habits (from now on, “gaming preferences”) on the effectiveness of a specific videogame that has been designed to increase the interest towards classical theater among teenagers. Using a validated instrument, participants were divided into four groups based on their gaming preferences: (1) Well-rounded (WR) gamers, who play all types of games often; (2) Hardcore players, who frequently tend to play first-person shooter (FPS) and sports games; (3) Casual players, who play moderately and tend to play music, social, and puzzle games; and (4) Non-gamers, who barely play videogames at all. Among all of the participants' personal factors (age, gender, and type of player) that were measured, only gaming preferences seemed to have a significant (p<;.05) positive influence on students' interest in theater-going. Neither age nor gender seemed to affect the outcomes. Casual and Well-rounded gamers scored higher in the game than Non-gamers and Hardcore players. Due to these results, we also explored whether the gaming profile affected traditional educational approaches. Traditional education worked better than videogames only for students who do not usually play videogames. This study suggests that gaming preferences may influence the effectiveness of different educational approaches. Knowing students' gaming preferences in advance may help educators find the best educational approach for each student.

This paper presents our perspective on serious games, covering relevant aspects of the current situation and what we consider are the key elements to be developed over the next few years. Serious games have undergone a long journey from niche research to application in real settings, coming closer to the generalization of games in mainstream education. We describe some of the lines of research we consider more relevant for completing this journey.

Oratory or the art of public speaking with eloquence has been cultivated since ancient times. However, the fear of speaking in public -a disproportionate reaction to the threatening situation of facing an audience- affects a very important part of the population. This work arises from the need to help alleviate this fear through a tool where to train the ability of public speaking. To this purpose, we built a virtual reality system that offers the speaker a safe environment to practice presentations. Since the audience is the only way to receive feedback when giving a speech, our system offer s a virtual audience that reacts and gives real-time feedback based on the emotions conveyed by three parameters: voice tone, speech content and speaker’s gaze. In this paper, we detail the modelling of a behavioural-realistic audience just focusing on the speakers’ voice tone: 1) by presenting an algorithm that controls the audience’ reactions based on the emotions beamed by the speaker, and 2) by carrying out an experiment comparing the reactions generated by the agents with those of a real audience to the same speech, in order to refine the given algorithm. In this experiment, the audience subjects are asked to fill a questionnaire - level of engagement and perceived emotions - for a speech performed by professional actors representing different emotions. Afterwards, we compared the reactions of said audience with the ones generated by our algorithm, and used the results to improve it.