Systematizing Games Learning Analytics for Serious Games

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  1. 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 EDUCON 2017
  2. 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/ EDUCON 2017
  3. 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 EDUCON 2017
  4. 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? EDUCON 2017 In entertaining games: Game Analytics (GA) In learning systems: Learning Analytics (LA)
  5. 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 (ngel 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/
  6. 6. 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
  7. 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 EDUCON 2017
  8. 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. 9. RAGE Analytics Dashboards EDUCON 2017
  10. 10. RAGE Analytics Alerts and warnings EDUCON 2017 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. 11. GLA architecture and technologies EDUCON 2017
  12. 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! EDUCON 2017
  13. 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 EDUCON 2017
  14. 14. Main References [1] xAPI tracking model (with ADL): ngel Serrano-Laguna, Ivn Martnez-Ortiz, Jason Haag, Damon Regan, Andy Johnson, Baltasar Fernndez-Manjn (2017): Applying standards to systematize learning analytics in serious games. Computer Standards & Interfaces 50 (2017) 116123. [2] Game Learning Analytics: Manuel Freire, ngel Serrano-Laguna, Borja Manero, Ivn Martnez-Ortiz, Pablo Moreno-Ger, Baltasar Fernndez-Manjn (2016): Game Learning Analytics: Learning Analytics for Serious Games. In Learning, Design, and Technology (pp. 129). Cham: Springer International Publishing. EDUCON 2017
  15. 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 EDUCON 2017