A Framework for Improving Accessibility of Serious Games in Handheld Augmented Reality Based on User Interaction Data

Serious games combined with augmented reality (AR) can positively impact user's motivation and learning experience. To ensure that users with different needs can use them effectively, we must ensure the accessibility of such solutions. The goal of this paper is to show that game-learning analytics can be utilized to identify issues for people with various needs and define ways to improve the accessibility of AR-based serious games. This paper presents a framework based on analysing automatically collected user interaction data in handheld AR-based serious games to adapt them to the user in the context of accessibility. An experiment was conducted with people with disabilities and people of different ages using the established framework. The focus of the experiment results analysis in this paper is on the evaluation of interaction mechanisms adapted to users based on their interaction data and preferences. The results showed that for most participants the assigned interaction mechanisms for virtual object transformation tasks (translation, scaling, rotation) in AR were satisfactory. The framework proposed in this paper provides the basis for further research in this field concerning the use of advanced analytics that enable intelligent adaptation to the individual user.