Ability and Context Based Adaptive System: A Proposal for Machine Learning Approach

TL;DR

This paper proposes an adaptive system that uses machine learning to monitor user performance and context, aiming to reduce errors and improve user satisfaction on small screen devices.

Contribution

It introduces a novel approach for predicting user errors and adapting interfaces based on machine learning, integrating user performance and contextual data.

Findings

Literature review on adaptive interfaces

Research questions for system development

Framework for machine learning-based adaptation

Abstract

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.