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Game Learning Analytics: Learning Analytics for Serious Games

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Learning, Design, and Technology

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.

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Acknowledgments

This work has been partially funded by Regional Government of Madrid (eMadrid S2013/ICE-2715), by the Complutense University of Madrid (GR3/14-921340), by the Ministry of Education (TIN2013-46149-C2-1-R and FPU12/04310), by the RIURE Network (CYTED 513RT0471), and by the European Commission (RAGE H2020-ICT-2014-1-644187, BEACONING H2020-ICT-2015-687676).

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Authors and Affiliations

  1. Department of Software Engineering and Artificial Intelligence, Universidad Complutense de Madrid, C/Profesor José Garcia Santesmases 9, 28040, Madrid, Spain

    Manuel Freire, Ángel Serrano-Laguna, Borja Manero Iglesias, Iván Martínez-Ortiz, Pablo Moreno-Ger & Baltasar Fernández-Manjón

Authors
  1. Manuel Freire
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  2. Ángel Serrano-Laguna
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  3. Borja Manero Iglesias
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  4. Iván Martínez-Ortiz
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  5. Pablo Moreno-Ger
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  6. Baltasar Fernández-Manjón
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Corresponding author

Correspondence to Manuel Freire .

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Editors and Affiliations

  1. College of Info, Ste G150, Univ of North Texas, Denton, Texas, USA

    Michael J Spector

  2. School of Education, Virginia Tech, Blacksburg, Virginia, USA

    Barbara B Lockee

  3. School of Education, Baker University, Overland Park, Kansas, USA

    Marcus D. Childress

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Freire, M., Serrano-Laguna, Á., Iglesias, B.M., Martínez-Ortiz, I., Moreno-Ger, P., Fernández-Manjón, B. (2016). Game Learning Analytics: Learning Analytics for Serious Games. In: Spector, M., Lockee, B., Childress, M. (eds) Learning, Design, and Technology. Springer, Cham. https://doi.org/10.1007/978-3-319-17727-4_21-1

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  • DOI: https://doi.org/10.1007/978-3-319-17727-4_21-1

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  • Online ISBN: 978-3-319-17727-4

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