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.

1. Systematizing
game learning analytics
for serious games
Cristina Alonso, Antonio Calvo, Manuel Freire, Ivan Martinez-Ortiz
Baltasar Fernandez-Manjon, balta@fdi.ucm.es - @BaltaFM
Grupo e-UCM www.e-ucm.es
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2. Serious games
Games are used in different fields such as
military, medicine, science…
They provide several benefits:
engaging, goal-oriented.
Serious games main purpose is not
to entertain but to
- learn
- change attitude or behavior
- create awareness of an issue
https://www.americasarmy.com/
http://www.aislados.es/
http://play.centerforgamescience.org/treefrog/
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3. Serious games issues
Usually serious games effectiveness is measured through pre-post tests
But actual learning takes place
during in-game interactions
How to measure game effectiveness?
Games usually have a black box model (only score)
No information about what is happening inside the game while
the user is playing
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4. Analytics and Game Learning Analytics
Game Learning Analytics
(GLA) for Serious Games:
- collect, analyze and visualize
data from learners’ interactions
Can GLA be systematized?
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➔ In entertaining games:
Game Analytics (GA)
➔ In learning systems:
Learning Analytics (LA)

5. First step: Data tracking
But data collection for analytics lacks of standards.
New standard interactions model
developed and implemented in
Experience API (xAPI) with ADL
(Ángel Serrano et al, 2017).
The model allows tracking of all
in-game interactions as xAPI traces
(e.g. level started or completed,
interactions with NPC or game items,
options selected, score increased)
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https://www.adlnet.gov/xapi/

6. Data tracking with xAPI for SG
xAPI model for serious
games developed by
e-UCM research group in
collaboration with ADL
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https://www.adlnet.gov/serious-games-cop

7. H2020 EU RAGE Project simplifies the
creation of SGs via ready-to-use assets
➔ game tracker and analytics server
Traces in xAPI are sent to the
RAGE Analytics server for their analysis.
➔ general game-independent analysis & visualizations
provided
➔ possible to configure game-dependent analysis
Next steps: Data analysis and visualization
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8. Data analysis: game (in)dependent
Systematization of GLA: set of game-independent analysis
provided for specific stakeholders:
- teachers: players’ progress, players’ errors
- developers: times of completion, videos seen/skipped
- students: final results, errors made
As long as traces follow xAPI format, these analysis do not
require further configuration!
Also possible to configure game-dependent analysis and
visualizations for specific games and game characteristics.

9. RAGE Analytics Dashboards
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10. RAGE Analytics Alerts and warnings
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From the game dependent
analytics specific
Alerts and Warnings can
be generated
e.g. teachers gain insight
and real-time control of
their classes when
deploying games

11. GLA architecture and technologies
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12. Current work: uAdventure
Previous game engine eAdventure (in Java).
● Helps to create educational
point & click adventure games
● Users do not need to program
Platform updated to uAdventure (in Unity).
Full integration of game learning analytics
into uAdventure authoring tool
No extra effort required to integrate default analytics into uAdventure games!
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13. Conclusions
Main results:
➔ Game Learning Analytics systematization for games
➔ Tracking profile developed and implemented in xAPI (in collab with ADL)
➔ Complete GLA architecture from data tracking to visualization
but we are still working to create new products ….
➔ Game authoring tool uAdventure for easy development, adapted from
previously successfully tested game engine eAdventure
➔ Complete integration of GLA and uAdventure
Further information: https://github.com/e-ucm/rage-analytics/wiki
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14. Main References
[1] xAPI tracking model (with ADL):
Ángel Serrano-Laguna, Iván Martínez-Ortiz, Jason Haag, Damon Regan, Andy Johnson, Baltasar
Fernández-Manjón (2017): Applying standards to systematize learning analytics in serious games.
Computer Standards & Interfaces 50 (2017) 116–123.
[2] Game Learning Analytics:
Manuel Freire, Ángel Serrano-Laguna, Borja Manero, Iván Martínez-Ortiz, Pablo Moreno-Ger, Baltasar
Fernández-Manjón (2016): Game Learning Analytics: Learning Analytics for Serious Games. In
Learning, Design, and Technology (pp. 1–29). Cham: Springer International Publishing.
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15. Thank you!
Any questions?
- Mail: balta@fdi.ucm.es
- Twitter: @BaltaFM
- GScholar: https://scholar.google.es/citations?user=eNJxjcwAAAAJ&hl=en
- ResearchGate: https://www.researchgate.net/profile/Baltasar_Fernandez-Manjon
- SlideShare: https://www.slideshare.net/BaltasarFernandezManjon
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