Ability and Context Based Adaptive System: A Proposal for Machine Learning Approach
This addresses usability issues for users of small screen devices, but it is incremental as it builds on existing adaptive system concepts.
The paper tackles the problem of user errors on small screen devices by proposing a system that predicts and learns from errors based on user interaction and contextual factors, aiming to adapt the user interface to improve performance and satisfaction.
When we interact with small screen devices, sometimes we make errors, due to our abilities/disabilities, contextual factors that distract our attention or problems related to the interface. Recovering from these errors may be time consuming or cause frustration. Predicting and learning these errors based on the previous user interaction and contextual factors, and adapting user interface to prevent from these errors can improve user performance and satisfaction. In this paper, we propose a system that aims to monitor user performance and contextual changes and do adaptations based on the user performance by using machine learning techniques. Here, we briefly present our systematic literature review findings and discuss our research questions towards developing such an adaptive system.