Split menus

Abstract

When some items in a menu are selected more frequently than others, as is often the case, designers or individual users may be able to speed performance and improve preference ratings by placing several high-frequency items at the top of the menu. Design guidelines for split menus were developed and applied. Split menus were implemented and tested in two in situ usability studies and a controlled experiment. In the usability studies performance times were reduced by 17 to 58% depending on the site and menus. In the controlled experiment split menus were significantly faster than alphabetic menus and yielded significantly higher subjective preferences. A possible resolution to the continuing debate among cognitive theorists about predicting menu selection times is offered. We conjecture and offer evidence that, at least when selecting items from pull-down menus, a logarithmic model applies to familiar (high-frequency) items, and a linear model to unfamiliar (low-frequency) items.

Investigating UI Displacements in an Adaptive Mobile Homescreen

“…Sears and Shneiderman (1994) created Split Menus for desktop applications, and demonstrated that selection time can be decreased by moving or copying the top 4 frequently used apps to the top of the menu. Findlater and McGrenere (2004) compared static, adaptive and adaptable split menus, with the naturally generated data of a single MS Office user.…”

Highlighting items as means of adaptive assistance

“…Authors associate this frustration to a reduction of user's confidence in the system, leading to a reduction in trust (Sears & Shneiderman, 1994;Tiernan et al, 2001). With regard to adaptive assistance, according to Hook (2000), user's trust in the system will decrease drastically following the initial incorrect advice provided by the adaptive system.…”

“…When Spatial adaptation is applied (such as split menus), the structure or content of the interface is not constant, resulting in the user frequently needing to adapt to the different layouts (Sears & Shneiderman, 1994). Graphical adaptation reduces a visual search time by using graphical techniques such as background colors (Gajos et al, 2006;Tsandilas & schraefel, 2005;Findlater & McGrenere, 2008b) and transparency without changing the structure of the interface (Findlater et al, 2009).…”

Statistical User Interfaces

“…Their position appears to be more or less randomly. Thus, Sears and Shneiderman (1994) found that bringing only the most used items to the top of the menu is an effective technique.…”