Model driven approach for adapting user interfaces to the context of accessibility: case of visually impaired users
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A critical success factor for ubiquitous user interfaces is the extent to which they can adapt to changing situations. Currently, the adaptive behavior of such user interfaces is defined by designers and developers at design time. However, what designers might have sketched to be a useful adaptation could be perceived by users as a disturbing system behavior or an adaptation that does not match with user's expectation. Thus, in this paper we introduce our approach to enhance the trust of the user into automatic context sensitive layout generation.
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An analysis of context-aware user interfaces shows that adaptation mechanisms have a cost-benefit trade-off for usability. Unpredictable autonomous interface adaptations can easily reduce a system's usability. To reduce this negative effect of adaptive behaviour, we have attempted to help users building adequate mental models of such systems. A user support concept was developed and applied to a context-aware mobile device with an adaptive user interface. The approach was evaluated with users and as expected, the user support improved ease of use, but unexpectedly it reduced learnability. This shows that an increase of ease of use can be realised without actually improving the user's mental model of adaptive systems.
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With the growing need for intelligent software, exploring the potential of Machine Learning (ML) algorithms for User Interface (UI) adaptation becomes an ultimate requirement. The work reported in this paper aims at enhancing the UI interaction by using a Rule Management Engine (RME) in order to handle a training phase for personalization. This phase is intended to teach to the system novel adaptation strategies based on the end-user feedback concerning his interaction (history, preferences...). The goal is also to ensure an adaptation learning by capitalizing on the user feedbacks via a promoting/demoting technique, and then to employ it later in different levels of the UI development.
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Intelligent User Interface (IUI) is an emerging interdisciplinary research area that focuses on improving the usability of existing user interfaces. Adaptive menus are the part of the IUI that is trying to improve existing menus’ usability by reducing the selection time. This paper surveys the most relevant studies that are carried out in this field. First, it introduces an Adaptive User Interface (AUI) and adaptive menus then describe various adaptation styles and adaptation policies that are being used in adaptive menus along with their benefits and drawbacks. It then lists the applications of adaptive systems and how they can be used, as well as the limitations and future direction of the work.
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Adaptive user interfaces are commonly used for providing different layouts and information according to the current context-of-use. However, the complexity and heterogeneity of potential users, platforms and environments lead to a combinatorial explosion of variants, making it almost impossible to foresee all potential results of adaptations at design time. In this paper we present our work on the automatic evaluation of usability aspects of adaptive user interfaces at runtime which is supposed to be used complementary to design time usability evaluation. We show how a user interface model, providing different adaptation alternatives, can be combined with a model of the current user to simulate interaction and evaluate the feasibility of different adaptations.
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An analysis of context-aware user interfaces shows that adaptation mechanisms have a cost-benefit trade-off for usability. Unpredictable autonomous interface adaptations can easily reduce a system's usability. To reduce this negative effect of adaptive behaviour, we have attempted to help users building adequate mental models of such systems. A user support concept was developed and applied to a context-aware mobile device with an adaptive user interface. The approach was evaluated with users and as expected, the user support improved ease of use, but unexpectedly it reduced learnability. This shows that an increase of ease of use can be realised without actually improving the user's mental model of adaptive systems.
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