Video games have become one of the largest entertainment industries, and their power to capture the attention of players worldwide soon prompted the idea of using games to improve education. However, these

Data science techniques, nowadays widespread across all fields, can also be applied to the wealth of information derived from student interactions with serious games. Use of data science techniques can greatly improve the evaluation of games, and allow both teachers and institutions to make evidence-based decisions. This can increase both teacher and institutional confidence regarding the use of serious games in formal education, greatly raising their attractiveness. This paper presents a systematic literature review on how authors have applied data science techniques on game analytics data and learning analytics data from serious games to determine: (1) the purposes for which data science has been applied to game learning analytics data, (2) which algorithms or analysis techniques are commonly used, (3) which stakeholders have been chosen to benefit from this information and (4) which results and conclusions have been drawn from these applications. Based on the categories established after the mapping and the findings of the review, we discuss the limitations of the studies analyzed and propose recommendations for future research in this field.

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 ¼ 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) Nongamers, 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.