Relation Between Multiple Intelligences and Game Preferences: an Evidence-Based Approach Pejman Sajjadi, Joachim Vlieghe and Olga De Troyer Vrije Universiteit Brussel, Department of Computer Science, WISE, Pleinlaan 2, Building F, 10th floor, 1050 Brussels BELGIUM Ssajjadi@vub.ac.be Joachim.Vlieghe@vub.ac.be Olga.DeTroyer@vub.ac.be Abstract: There is a bulk of research demonstrating that individualized instruction yields much better results than the traditional uniform approach of one-size-fits-all (Vandewaetere et al, 2011). An interesting approach to understanding individual differences among learners is Gardner’s theory of Multiple Intelligences, stating that the intelligence of people is multi-dimensional and people have a unique blend of intelligences. According to Gardner the big challenge in education is how to best take advantage of this uniqueness. In the literature, it is suggested that people with different intellectual strengths often exhibit clear preferences toward specific modalities and types of interaction in relation to learning. This raises the question whether this knowledge could be transferred and employed in the design of (serious) games, i.e., do players with different intellectual strengths have preference for different games or game constructs and can these preferences be used to improve the game and/or learning experience? Various theoretical claims regarding these issues have been made but have hardly been substantiated with empirical evidence. We take a step towards that end as we empirically investigate whether individual differences in terms of Gardner’s multiple intelligences correlate with differences in terms of game preferences. This is performed by means of an online survey study among 308 avid gamers. We found that individual differences in terms of intelligences do in fact correlate with preferences for specific games. The results of the study also show that the correlations between players’ intelligences and game preferences cannot be simply explained by considering the genre of the games. This indicates that it will be necessary to look into more detail to the components of the games to be able to explain the preferences and identify what game characteristics are preferred by players exhibiting certain intelligences. Moreover, our results indicate that the theoretical mappings suggested in the literature can be refined and completed further based on the evidences we provide. Keywords: Multiple intelligences; Game preferences; Evidence-based 1. Introduction The theory of Multiple Intelligence (MI) (Gardner, 2011) states that human’s intelligence is multi-dimensional, as opposed to the one-dimensional understanding of intelligence represented as Intelligence Quotient (IQ). In MI eight different dimensions are recognized. Everyone possesses every intelligence but to different degrees. Chan (2005) suggests that people with different intelligences or intellectual strengths often exhibit clear preferences toward specific modalities and types of interaction in relation to learning and self-expression. This raises the question whether this knowledge could be transferred and employed in the design of (serious) games. Offering serious games adapted to players’ intelligences is likely to improve gameplay experience, potentially resulting in an increase of attention and motivation which may ultimately result in increased learning outcomes. Research (Millis et al, 2011; Poels et al, 2007) argue that good gameplay experience could lead to a state of absolute absorption into a task to a point of losing self-consciousness. In this flow state, the activity itself becomes rewarding in its own and enables individuals to function at their fullest capacity (Csikszentmihalyi & Csikszentmihalyi, 1992) including the capacity to learn (Webster et al, 1993; Graesser et al, 2008). Various scholars (McCue, 2005; Becker, 2007; Jing et al, 2012; Chuang & Sheng-Hsiung, 2012; Starks, 2014; Lepe-salazar, 2015) have argued that exploiting the potential relation between MI and game constructs could prove to be important for personalized or player-centred game design. Unfortunately, while using MI for personalized or player-centred game design seems promising, empirical evidence on the applicability and effectiveness of this approach is virtually non-existent. Such evidence is crucial to support game developers in making informed decision about which modalities, interaction patterns and game mechanics to incorporate in their design when taking player’s intelligences into consideration. 565