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Systematizing game learning analytics for serious games Cristina Alonso, Antonio Calvo, Manuel Freire, Ivan Martinez-Ortiz Baltasar Fernandez-Manjon, [email protected] - @BaltaFM Grupo e-UCM www.e-ucm.es EDUCON 2017

Systematizing Games Learning Analytics for Serious Games

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Systematizing game learning analytics

for serious gamesCristina Alonso, Antonio Calvo, Manuel Freire, Ivan Martinez-Ortiz

Baltasar Fernandez-Manjon, [email protected] - @BaltaFM

Grupo e-UCM www.e-ucm.es

EDUCON 2017

Serious gamesGames are used in different fields such asmilitary, medicine, science…

They provide several benefits: engaging, goal-oriented.

Serious games main purpose is notto 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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Serious games issuesUsually 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 whilethe user is playing

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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)

First step: Data trackingBut 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/

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

H2020 EU RAGE Project simplifies thecreation of SGs via ready-to-use assets➔ game tracker and analytics serverTraces 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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Data analysis: game (in)dependentSystematization 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.

RAGE Analytics Dashboards

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RAGE Analytics Alerts and warnings

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From the game dependent analytics specificAlerts and Warnings can be generated

e.g. teachers gain insight and real-time control of their classes when deploying games

GLA architecture and technologies

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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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ConclusionsMain 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/wikiEDUCON 2017

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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Thank you!

Any questions?

- Mail: [email protected] 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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