Using game learning analytics for validating the design of a learning game for adults with intellectual disabilities

Abstract

AbstractSerious Games (SGs), 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. 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 SG for training students with Intellectual Disabilities in traveling around the subway as a complement to traditional training. Fifty‐one 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 (ie, 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.