Baltasar Fernández-Manjón

Game Learning Analytics: Learning Analytics for Serious Games

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 educational games, commonly referred to as serious games, face different challenges when brought into the classroom, ranging from pragmatic issues (e.g. a high development cost) to deeper educational issues, including a lack of understanding of how the students interact with the games and how the learning process actually occurs. This chapter explores the potential of data-driven approaches to improve the practical applicability of serious games. Existing work done by the entertainment and learning industries helps to build a conceptual model of the tasks required to analyze player interactions in serious games (gaming learning analytics or GLA). The chapter also describes the main ongoing initiatives to create reference GLA infrastructures and their connection to new emerging specifications from the educational technology field. Finally, it explores how this data-driven GLA will help in the development of a new generation of more effective educational games and new business models that will support their expansion. This results in additional ethical implications, which are discussed at the end of the chapter.

Multi-Level Game Learning Analytics for Serious Games

Serious games are usually used or deployed in an educational setting as an isolated or individual activity, disconnected from other curricular activities. However, to really increase the adoption of serious games in different educational scenarios, the combination and integration of games into the educational flow should be simplified. We envision Serious Games as new type of educational activity that can be combined as parts of other games (e.g. minigames integrated in larger games), integrated into other online activities, or even mixed with both game and non-game activities. In addition, if we want to make the most from serious games, a learning analytics system must be in place to harvest and analyze interactions, providing metrics and insights to instructors regarding the gameplay sessions. Moreover, if a course-level learning analytics strategy is designed, it must be aligned with the game learning analytics. This approach requires communication between games and educational activities used during the educational experience. From a game learning analytics standpoint, gaining insights from these integrated experiences introduces new requirements within potentially complex multi-level or hierarchical activities. Moreover, the analysis required to generate these metrics should be both efficient and provide insight in an understandable way and for different stakeholders. This paper describes an approach to multilevel game learning analytics from the perspectives of data model, implementation architecture, and result visualization in teacher-oriented dashboards