The mean task execution times for all types increase dramatically for the independent overlapping windows treatment as the task environment complexity (i.e the number of windows) increase. However, this increase is less than linear for the Elastic Windows interface. These results indicate that Elastic Windows is more scalable compared to the independent overlapping windows approach.
In sequential scan, having a stable layout during the task execution helps subjects greatly. In Elastic Windows, windows are well-organized, side by side and during task execution subjects do not find it necessary to manipulate (i.e. resize, move) the windows. However, in the independent overlapping windows the layout is continuously changing, windows are raised, moved and resized frequently due to the limited screen space. Subjects produce dramatic changes from their initial layout during task execution. These disruptive changes are more prevalent as the task environment complexity increased.
In comparison, having windows side by side in Elastic Windows helps users to compare window contents easily. Since windows are well organized, users adopted a visual approach in comparing window contents and eliminated some windows immediately. However, in the independent overlapping windows interface, users have to look at each window one by one, changing the layout constantly which made it harder to do the comparison after a while, since the locations of previously examined windows are harder to recall. The situation is more severe in the high complexity task environment. Users perform a comparison task in 10.9 seconds with Elastic Windows and in 135.4 seconds with independent overlapping windows, thereby achieving more than ten-fold performance speed-up.
In determine context + scan, subjects using Elastic Windows maximized a subset of the windows belonging to the determined context, enabling them to focus on the context more easily due to the larger screen space allocated. In independent overlapping windows however, subjects spend time to reorganize the layout, relying on their spatial memory for the location of windows belonging to the context.
Recall is easier in the Elastic Windows interface because of the more
stable window organization across task executions. Subjects state that
it is easier to remember window locations with Elastic Windows
compared to the independent overlapping windows. Since the window
organization is modified in the overlapping windows interface for each
task execution in the session, the spatial memory of users is lost. In
the low complexity task environment with only two windows on the
screen, it is not difficult to recall window locations in either
approaches, as expected.
A few of the subjects have a task execution with comparably low performance in the Elastic Windows interface, where subjects have hard times thinking of a strategy for the task. This is likely due to the relative inexperience of the subjects with the Elastic Windows interface as opposed to their lengthy experience with the popular independent overlapping windows interfaces.
In summary, multi-window operations facilitated by the hierarchical organization of windows are useful to perform faster task environment setup, switching, and task executions compared to single window operations in current approaches. These results suggest promising possibilities for multi-window operations and hierarchical nesting, which can be applied to the next generation of tiled as well as overlapped window managers. They should enable users to more readily deal with increasingly complex tasks. Especially tasks with multiple windows are likely to benefit from multiple window strategies.