Living Reference Work Entry

Learning, Design, and Technology

pp 1-29

Date: Latest Version

Game Learning Analytics: Learning Analytics for Serious Games

  • Manuel FreireAffiliated withDepartment of Software Engineering and Artificial Intelligence, Universidad Complutense de Madrid Email author 
  • , Ángel Serrano-LagunaAffiliated withDepartment of Software Engineering and Artificial Intelligence, Universidad Complutense de Madrid
  • , Borja Manero IglesiasAffiliated withDepartment of Software Engineering and Artificial Intelligence, Universidad Complutense de Madrid
  • , Iván Martínez-OrtizAffiliated withDepartment of Software Engineering and Artificial Intelligence, Universidad Complutense de Madrid
  • , Pablo Moreno-GerAffiliated withDepartment of Software Engineering and Artificial Intelligence, Universidad Complutense de Madrid
  • , Baltasar Fernández-ManjónAffiliated withDepartment of Software Engineering and Artificial Intelligence, Universidad Complutense de Madrid

Abstract

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 (game 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.

Keywords

Serious games Game learning analytics Learning analytics Game analytics Educational standards