Upload
baltasar-fernandez-manjon
View
422
Download
0
Embed Size (px)
Citation preview
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/
EDUCON 2017
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
EDUCON 2017
Analytics and Game Learning Analytics
Game Learning Analytics (GLA) for Serious Games: - collect, analyze and visualize data from learners’ interactions
Can GLA be systematized?
EDUCON 2017
➔ 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)
EDUCON 2017
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
EDUCON 2017
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
EDUCON 2017
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 Alerts and warnings
EDUCON 2017
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
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!
EDUCON 2017
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.
EDUCON 2017
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
EDUCON 2017