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.

Game learning analytics (GLA) comprise the collection, analysis, and visualization of player interactions with serious games. The information gathered from these analytics can help us improve serious games and better understand player actions and strategies, as well as improve player assessment. However, the application of analytics is a complex and costly process that is not yet generalized in serious games. Using a standard data format to collect player interactions is essential: the standardization allows us to simplify and systematize every step in developing tools and processes compatible with multiple games. In this paper, we explore a combination of 1) an exploratory visualization tool that analyzes player interactions in the game and provides an overview of their actions, and 2) an assessment approach, based on the collection of interaction data for player assessment. We describe some of the different opportunities offered by analytics in game-based learning, the relevance of systematizing the process by using standards and game-independent analyses and visualizations, and the different techniques (visualizations, data mining models) that can be applied to yield meaningful information to better understand learners’ actions and results in serious games.