engleski

On Efficiency of Adaptation Algorithms for Mobile Interfaces Navigation

Many ubiquitous computing systems and applications, including mobile learning ones, can make use of personalization procedures in order to support and improve universal usability. In our previous work, we have created a GUI menu model for mobile device applications, where personalization capabilities are primarily derived from the use of adaptable and adaptive techniques. In this paper we analyze from a theoretical point of view the efficiency of the two adaptation approaches and related algorithms. A task simulation framework has been developed for comparison of static and automatically adapted menus in the mobile application environment. Algorithm functionality is evaluated according to adaptivity effects provided in various menu configurations and within several classes of randomly generated navigation tasks. Simulation results thus obtained support the usage of adaptivity, which provides a valuable improvement in navigation efficiency within menu-based mobile interfaces.

personalization; adaptation; algorithmics; m-devices; m-learning

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