Aplicación de analíticas en la sistematización del diseño y validación de juegos serios para usuarios con discapacidad intelectual

En los últimos años, los Serious Games (juegos serios), también conocidos por otros muchos nombres como Learning Games o Educational Games (juegos educativos), se han convertido en herramientas de aprendizaje ampliamente utilizadas, de modo que hay muchos proyectos de desarrollo y de aplicación relacionados con este tipo de juegos. Sin embargo, se necesita más investigación sobre cómo crear diseños efectivos que optimicen el proceso de desarrollo y garanticen la adecuación de estos juegos a las habilidades cognitivas de los usuarios.Este problema es especialmente relevante cuando se quiere desarrollar un juego educativo para personas con discapacidad intelectual. Dado que este tipo de usuarios cuentan típicamente con problemas de comunicación, la utilización de métodos observacionales es muy complejo, costoso e incluso poco fiable para evaluar y validar el diseño de los juegos..

Using Game Learning Analytics for Validating the Design of a Learning Game for Adults with Intellectual Disabilities.

Serious Games, defined as a game in which education (in its various forms) is the primary goal rather than entertainment, have been proven as an effective educational tool for engaging and motivating students (Michael & Chen, 2006). However, more research is needed to sustain the suitability of these games to train users with cognitive impairments. This empirical study addresses the use of a Serious Game for training students with Intellectual Disabilities in traveling around the subway as a complement to traditional training. Fifty-one (51) adult people with Down Syndrome, mild cognitive disability or certain types of Autism Spectrum Disorder, all conditions classified as intellectual disabilities, played the learning game Downtown, A Subway Adventure which was designed ad-hoc considering their needs and cognitive skills. We used standards-based Game Learning Analytics techniques (i.e. Experience API –xAPI), to collect and analyze learning data both off-line and in near-real time while the users were playing the videogame. This article analyzes and assesses the evidence data collected using analytics during the game sessions, like time completing tasks, inactivity times or the number of correct/incorrect stations while traveling. Based on a multiple baseline design, the results validated both the game design and the tasks and activities proposed in Downtown as a supplementary tool to train skills in transportation. Differences between High-Functioning and Medium-Functioning users were found and explained in this paper, but the fact that almost all of the students completed at least one route without mistakes, the general improvement trough sessions and the low-mistake ratio are good indicators about the appropriateness of the game design.pre-print311 K

Game Analytics Evidence-Based Evaluation of a Learning Game for Intellectual Disabled Users.

Learning games are becoming popular among teachers as educational tools. However, despite all the game development quality processes (e.g., beta testing), there is no total assurance about the game design appropriateness to the students' cognitive skills until the games are used in the classroom. Furthermore, games designed speci cally for Intellectual Disabled (ID) users are even harder to evaluate because of the communication issues that this type of players have. ID users' feedback about their learning experience is complex to obtain and not always fully reliable. To address this problem, we use an evidence-based approach for evaluating the game design of Downtown, A Subway Adventure, a game created to improve independent living in users with ID. In this paper we exemplify the whole process of applying Game Analytics techniques to gather actual users' gameplay interaction data in real settings for evaluating the design. Following this process, researchers were able to validate different game aspects (e.g., mechanics) and could also identify game aws that may be dif cult to detect using formative evaluation or other observational-based methods. Results showed that the proposed evidence-based approach using Game Analytics information is an effective way to evaluate both the game design and the implementation, especially in situations where other types of evaluations that require users' involvement are limited.post-print1129 K