Exploring a Design Space of Graphical Adaptive Menus:Normal vs. Small Screens
(2020) A C M Transactions on Interactive Intelligent Systems — Vol. 10, n° 1, p. Article 2 (2020)
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- Abstract
- Graphical Adaptive Menus are Graphical User Interfaces menus whose items predicted of immediate usage can be automatically rendered in a prediction window. Rendering this prediction window is a key question for adaptivity to enable the end user to efficiently differentiate predicted items from normal ones and to select appropriate items consequently. Adaptivity for graphical menus has been more investigated for normal screens, such as desktops, than for small screens, such as smartphones, where real estate imposes severe rendering constraints. To address this question, this paper defines and explores a design space where graphical adaptive menus are structured based on Bertin’s eight visual variables (i.e., position, size, shape, value, color, orientation, texture, and motion) and their combination by comparing their rendering for small screens with respect to normal screens. Based on this design space, previously introduced graphical adaptive menus are revisited in terms of four stability properties (i.e., spatial, physical, format, and temporal), new menu designs are introduced and discussed for both normal and small screens. The resulting set of graphical adaptive menu has been subject to a preference analysis from which a particular design emerged: the cloud menu, where predicted items are arranged in an adaptive tag cloud. We investigate empirically the effect of the cloud menu on the item selection time and the error rate, with respect to a static menu and an adaptive linear menu. The paper then suggests a set of usability guidelines useful for designers and practitioners to design graphical adaptive menus in general and cloud menus in particular.
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Citations
Vanderdonckt, J., Bouzit, S., Calvary, G., & Chêne, D. (2020). Exploring a Design Space of Graphical Adaptive Menus:Normal vs. Small Screens. A C M Transactions on Interactive Intelligent Systems, 10(1), Article 2. https://doi.org/10.1145/3237190 (Original work published 2020)