Advertisement

GLAID: Designing a Game Learning Analytics Model to Analyze the Learning Process in Users with Intellectual Disabilities

  • Ana R. Cano
  • Baltasar Fernández-Manjón
  • Álvaro J. García-Tejedor
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 176)

Abstract

Educational Games are increasingly popular in teaching as they have proven to be effective learning tools. Educational videogames are beneficial for all kind of students but we think they are especially suited for users with intellectual disabilities due to the opportunity of tailoring the content to their in-game performance. Adapting the game experience to the cognitive and learning abilities to this type of students also make videogames a powerful source of learning data. In this paper we introduce the GLAID (Game Learning Analytics for Intellectual Disabilities) Model, a theoretical adaptation of a more general analytics framework. It describes how to collect, process and analyze videogame interaction data in order to provide an overview of the user learning experience, from an individualized assessment to a collective perspective. But to obtain these goals it is necessary to take into account the restrictions and special needs of users with intellectual disabilities both in the learning design and in translating them into game mechanics and the corresponding observables that will be collected for the subsequent data analysis. We conclude with a discussion and considerations about the model and future steps to follow in our investigation.

Keywords

Serious games Intellectual Disabilities Game Learning Analytics Analytics maturity framework Educational games Down syndrome Autism spectrum disorders 

Notes

Acknowledgements

The e-UCM research group has been partially funded by Regional Government of Madrid (eMadrid S2013/ICE-2715), by the Ministry of Education (TIN2013-46149-C2-1-R) and by the European Commission (RAGE H2020-ICT-2014-1-644187, BEACONING H2020-ICT-2015-687676). The CEIEC Institute has been partially funded by the Francisco de Vitoria University (UFV-2015-05).

References

  1. 1.
    Annetta, L., Minogue, J., Holmes, S., Cheng, M.: Investigating the impact of video games on high school students’ engagement and learning about genetics. Comput. Educ. 53, 74–85 (2009)CrossRefGoogle Scholar
  2. 2.
    Griffiths, M.: The educational benefits of videogames. Educ. Health 20(3), 47–51 (2002)Google Scholar
  3. 3.
    Campbell, D.: Xbox Data is XXL. In: Strata Conference 2013, Santa Clara, California, 28 February 2013. https://www.youtube.com/watch?v=ZLKf1AhLQ30&feature=youtu.be. Accessed 10 Mar 2016
  4. 4.
    Long, P., Siemenes, G.: Penetrating the fog: analytics in learning and education. In: 1st International Conference on Learning Analytics and Knowledge, Banff, Alberta, Canada (2011)Google Scholar
  5. 5.
    Freire, M., Serrano-Laguna, A., Manero, B., Martínez-Ortiz, I., Moreno-Ger, P., Fernández-Manjón, B.: Game learning analytics. In: Learning Analytics for Serious Games (2015)Google Scholar
  6. 6.
    Roberts, J., Price, J., Malkin, C.: Language and communication development in down syndrome. Ment. Retard. Dev. Disabil. Res. Rev. Spec. Issue: Lang. Commun. 13(1), 26–35 (2007)CrossRefGoogle Scholar
  7. 7.
    Gartner, Introduction to the Gartner Maturity Model for Web Analytics, 03 July 2008. https://www.gartner.com/doc/713210/introduction-gartner-maturity-model-web. Accessed 03 Mar 2016
  8. 8.
    Australian Government. Department of Families, Housing, Community Services and Indigenous Affairs, ‘‘Intellectual disabilities, communication and learning’’, 20 January 2009. http://resources.fahcsia.gov.au/consumertrainingsupportproducts/employers/intellectual_disability/sec3.htm. Accessed 11 Mar 2016
  9. 9.
    Arranz, P.: Niños y jóvenes con Síndrome de Down, Egido Editorial (2002)Google Scholar
  10. 10.
    Troncoso, M., Jesús, F.: Síndrome de Down: Avances en acción familiar, Santander: Fundación Síndrome de Down de Cantabria (1988)Google Scholar
  11. 11.
    Chapman, R., Hesketh, L.: Behavioral phenotype of individuals with Down Syndrome. Ment. Retard. Dev. Disabil. Res. Rev. 6(2), 84–95 (1999)CrossRefGoogle Scholar
  12. 12.
    W3C World Wide Web Consortium, ‘‘WCAG 2.0.’’ http://www.w3.org/TR/WCAG20/. Accessed 09 Mar 2016
  13. 13.
    Ablegamers Foundation, ‘‘Game Accessibililty Guidelines,’’ 2012–2015. http://gameaccessibilityguidelines.com/. Accessed 09 Mar 2016
  14. 14.
    Serrano-Laguna, A., Torrente, J., Moreno-Ger, P., Fernández-Manjón, B.: Tracing a little for big improvements: application of learning anañytics and videogames for student assessment. Procedia Comput. Sci. 15, 203–209 (2012)CrossRefGoogle Scholar
  15. 15.
    Nyce, C.: Predictive Analytics White Paper, American Institute for Chartered Property Casualty Underwriters/Insurance Institute of America (2007)Google Scholar

Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2017

Authors and Affiliations

  • Ana R. Cano
    • 1
  • Baltasar Fernández-Manjón
    • 1
  • Álvaro J. García-Tejedor
    • 2
  1. 1.Universidad Complutense de MadridMadridSpain
  2. 2.Universidad Francisco de VitoriaMadridSpain

Personalised recommendations