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Manuel freire seminario_uam_e_madrid

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Manuel freire seminario_uam_e_madrid

  1. 1. Analytics & Learning in Serious Games Manuel Freire Morán Grupo e-UCM de la Universidad Complutense de Madrid www.e-ucm.es Red eMadrid www.emadridnet.org
  2. 2. Table of Contents Overview  Analytics, Learning, Serious Games  e-UCM, RAGE, BEACONING & IMPRESS Zoom & Filter  Architecting Game Learning Analytics  Dashboards & Learning Analytics Models  Hierarchical models  Heterogeneous contexts Details on demand  Questions, comments? manuel.freire@fdi.ucm.es - Madrid, 2018.11.23 2 1 2 3
  3. 3. Analytics, Learning & SGs manuel.freire@fdi.ucm.es - Madrid, 2018.11.23 3 Freire, M., Martínez-Ortiz, I., & Fernández-Manjón, B. (2018). Making Understandable Game Learning Analytics for Teachers. In ICWL 2018, Chiang Mai, Thailand, August 22-24, 2018 https://doi.org/10.1007/978-3-319-96565-9_11
  4. 4. e-UCM, RAGE, BEACONING & IMPRESS manuel.freire@fdi.ucm.es - Madrid, 2018.11.23 4 210 papers since 2008 6 PhDs defended in last 7y (3 w/ awards) ERASMUS+, 2017-2020 5 partners 4 countries H2020, 2015-2018 5.9 M€ 16 partners H2020, 2014-2018 8.9 M€ 18 partners 10 countries
  5. 5. RAGE manuel.freire@fdi.ucm.es - Madrid, 2018.11.23 5 gamecomponents.eu
  6. 6. BEACONING manuel.freire@fdi.ucm.es - Madrid, 2018.11.23 6 beaconing.eu
  7. 7. IMPRESS manuel.freire@fdi.ucm.es - Madrid, 2018.11.23 7 code-defenders.org Formalz gameplay trailer https://www.youtube.com/watch?v=LYpxz0N7TpY Impress-project.org
  8. 8. Overview  Analytics, Learning, Serious Games  e-UCM, RAGE, BEACONING & IMPRESS Zoom & Filter  Architecting Game Learning Analytics  Dashboards & Learning Analytics Models  Hierarchical models  Heterogeneous contexts Details on demand  Questions, comments? manuel.freire@fdi.ucm.es - Madrid, 2018.11.23 8
  9. 9. Architecting GLA manuel.freire@fdi.ucm.es - Madrid, 2018.11.23 9
  10. 10. Architecture manuel.freire@fdi.ucm.es - Madrid, 2018.11.23 10
  11. 11. Case study: Conectado manuel.freire@fdi.ucm.es - Madrid, 2018.11.23 11 Evaluation with 257 students from 3 centers, ages 15-17 Calvo-Morata, A., Rotaru, D. C., Alonso-Fernández, C., Freire, M., Martínez-Ortiz, I., & Fernández-Manjón, B. (2018). Validation of a Cyberbullying Serious Game Using Game Analytics. IEEE Transactions on Learning Technologies. https://doi.org/10.1109/TLT.2018.2879354
  12. 12. Learning Analytics Models manuel.freire@fdi.ucm.es - Madrid, 2018.11.23 12 Perez-Colado, I., Alonso-Fernandez, C., Freire, M., Martinez-Ortiz, I., & Fernandez-Manjon, B. (2018, April). Game Learning Analytics is not informagic!. https://doi.org/10.1109/EDUCON.2018.8363443 Learning Analytics Model (LAM)
  13. 13. Customized Dashboards manuel.freire@fdi.ucm.es - Madrid, 2018.11.23 13 Freire, M., Martínez-Ortiz, I., & Fernández-Manjón, B. (2018). Making Understandable Game Learning Analytics for Teachers. ICWL 2018, Chiang Mai, Thailand, August 2018 https://doi.org/10.1007/978-3-319-96565-9_11
  14. 14. Hierarchical LAMs manuel.freire@fdi.ucm.es - Madrid, 2018.11.23 14 Perez-Colado, I., Alonso-Fernandez, C., Freire, M., Martinez-Ortiz, I., & Fernandez-Manjon, B. (2018, April). Game Learning Analytics is not informagic!. https://doi.org/10.1109/EDUCON.2018.8363443
  15. 15. Hierarchical analyses manuel.freire@fdi.ucm.es - Madrid, 2018.11.23 15 Ivan Perez-Colado, Dan C. Rotaru, Manuel Freire, Iván Martínez-Ortiz, Baltasar Fernández-Manjón (2018): Multi-level Game Learning Analytics for Serious Games. VS Games 2018. Würzburg, Germany. https://doi.org/10.1109/VS-Games.2018.8493435
  16. 16. Hierarchical dashboards manuel.freire@fdi.ucm.es - Madrid, 2018.11.23 16 Ivan Perez-Colado, Dan C. Rotaru, Manuel Freire, Iván Martínez-Ortiz, Baltasar Fernández-Manjón (2018): Multi-level Game Learning Analytics for Serious Games. VS Games 2018. Würzburg, Germany. https://doi.org/10.1109/VS-Games.2018.8493435
  17. 17. Heterogeneous contexts  Analytics for a set of disparate activities?  Each manages their own authorization & authentication  Some have no analytics at all, while others keep their own  No plans to use LMS: LTI impractical  Case study: IMPRESS  Configuration & launch through an Activity Manager  Unified authentication & authorization via SAML  Simplified class & activity setup in Activity Manager  Analysis re-configuration using  Kappa architecture  On-the-fly calculations specified directly in the dashboard manuel.freire@fdi.ucm.es - Madrid, 2018.11.23 17
  18. 18. Overview  Analytics, Learning, Serious Games  e-UCM, RAGE, BEACONING & IMPRESS Zoom & Filter  Architecting Game Learning Analytics  Dashboards & Learning Analytics Models  Hierarchical models  Heterogeneous contexts Details on demand  Questions, comments? manuel.freire@fdi.ucm.es - Madrid, 2018.11.23 18
  19. 19. manuel.freire@fdi.ucm.es - Madrid, 2018.11.23 19
  20. 20. Evaluating & Predicting manuel.freire@fdi.ucm.es - Madrid, 2018.11.23 20 Experiment with 227 students on a previously-validated game Cristina Alonso-Fernández, Iván Martínez-Ortiz, Rafael Caballero, Manuel Freire and Baltasar Fernández-Manjón, Predicting students’ knowledge after playing a serious game based on learning analytics data, (Manuscript under review)

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