GLAID: Designing a Game Learning Analytics Model to Analyze the Learning Process in Users with Intellectual Disabilities
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.
KeywordsSerious games Intellectual Disabilities Game Learning Analytics Analytics maturity framework Educational games Down syndrome Autism spectrum disorders
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).
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