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.

-Applying games in education provides multiple benefits clearly visible in entertainment games: their engaging, goal-oriented nature encourages students to improve while they play. Educational games, also known as Serious Games (SGs) are video games designed with a main purpose other than pure entertainment; their main purpose may be to teach, to change an attitude or behavior, or to create awareness of a certain issue. As educators and game developers, the validity and effectiveness of these games towards their defined educational purposes needs to be both measurable and measured. Fortunately, the highly interactive nature of games makes the application of Learning Analytics (LA) perfect to capture students' interaction data with the purpose of better understanding or improving the learning process. However, there is a lack of widely adopted standards to communicate information between games and their tracking modules. Game Learning Analytics (GLA) combines the educational goals of LA with technologies that are commonplace in Game Analytics (GA), and also suffers from a lack of standards adoption that would facilitate its use across different SGs. In this paper, we describe two key steps towards the systematization of GLA: 1), the use of a newly-proposed standard tracking model to exchange information between the SG and the analytics platform, allowing reusable tracker components to be developed for each game engine or development platform; and 2), the use of standardized analysis and visualization assets to provide general but useful information for any SG that sends its data in the aforementioned format. These analysis and visualizations can be further customized and adapted for particular games when needed. We examine the use of this complete standard model in the GLA system currently under development for use in two EU H2020 SG projects.

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Serious games have proven to be a powerful tool in education to engage, motivate, and help students learn. However, the change in student knowledge after playing games is usually measured with traditional (paper) prequestionnaires–postquestionnaires. We propose a combination of game learning analytics and data mining techniques to predict knowledge change based on in‐game student interactions. We have tested this approach in a case study for which we have conducted preexperiments–postexperiments with 227 students playing a previously validated serious game on first aid techniques. We collected student interaction data while students played, using a game learning analytics infrastructure and the standard data format Experience API for Serious Games. After data collection, we developed and tested prediction models to determine whether knowledge, given as posttest results, can be accurately predicted. Additionally, we compared models both with and without pretest information to determine the importance of previous knowledge when predicting postgame knowledge. The high accuracy of the obtained prediction models suggests that serious games can be used not only to teach but also to measure knowledge acquisition after playing. This will simplify serious games application for educational settings and especially in the classroom easing teachers' evaluation tasks.

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Bullying is a serious social problem at schools, very prevalent independently of culture and country, and particularly acute for teenagers. With the irruption of always-on communications technology, the problem, now termed cyberbullying, is no longer restricted to school premises and hours. There are many different approaches to address cyberbullying, such as school buddies, educational videos, or involving police in counseling; however, awareness continues to be insufficient. We have developed Conectado, a serious game to be used in the classroom to increase awareness on bullying and cyberbullying in schools. While playing the game, students gain a first-hand immersive experience of the problem and the associated emotions, fostering awareness and empathy with victims. This paper describes Conectado and presents its validation with actual students using game analytics.

Learning games are becoming popular among teachers as educational tools. However, despite all the game development quality processes (e.g., beta testing), there is no total assurance about the game design appropriateness to the students' cognitive skills until the games are used in the classroom. Furthermore, games designed specifically for Intellectual Disabled (ID) users are even harder to evaluate because of the communication issues that this type of players have. ID users' feedback about their learning experience is complex to obtain and not always fully reliable. To address this problem, we use an evidence-based approach for evaluating the game design of Downtown, A Subway Adventure, a game created to improve independent living in users with ID. In this paper we exemplify the whole process of applying Game Analytics techniques to gather actual users' gameplay interaction data in real settings for evaluating the design. Following this process, researchers were able to validate different game aspects (e.g., mechanics) and could also identify game flaws that may be difficult to detect using formative evaluation or other observational-based methods. Results showed that the proposed evidence-based approach using Game Analytics information is an effective way to evaluate both the game design and the implementation, especially in situations where other types of evaluations that require users' involvement are limited.