We conducted an experiment to compare the performance and user preferences for the Taskbar (Figure 5.6),Alt+Tab (Figure 5.5),Direct pointing,Expos´e (Figure 5.4) and stack scanning (Figure 5.3) techniques in different scenarios.
Stack Scanning Widget
Figure 5.3: Stack scanning with 7 layers, us- ing the original windows as representation.
Figure 5.4: Expos´e view of 8 windows, rep- resenting them as thumbnails.
5.4.1 Visual Factors For Window Switching
There are many visual factors that can impact the performance of window switching techniques, including the number of windows, windows layout, visual similarity.
Number of windows. Probably the best-studied factors in window switching techniques is this factor.
Windows Layout. The performance of window switching techniques heavily depends on the windows layout, which is also the most difficult to consider. To quantify this factor,
5.4 Experiment
Figure 5.5: Alt+Tab represents windows as icons.
Figure 5.6: Taskbar in each of the 8, 12 and 16 windows conditions withNVS, representing windows as buttons.
we use three factors: window size, amount of overlapping and window Z-order to define a windows layout.
• Window size. It can directly impact the time of visual search and selection. We divided windows into three categories by window size: SmallWindow (the window size is less than 25% of screen resolution), NormalWindow (between 25% and 75%), LargeWindow (greater than 75%);
• Amount of overlapping. It may impact the visibility of windows, and this can directly impact the performance of visual search. Meanwhile the amount of over- lapping determines the number of layers in stack scanning technique;
• Window Z-order. It can affect the duration of operations when the technique de- pends on this factor (such as, Alt+Tab)
Visual similarity. Window switching techniques use various visualizations to repre- sent windows (such as, icon+title, thumbnail), different visualizations have different visual stimulus for users. We divide visual similarity between windows into three types: un- similar (NVS)(different application type, the title, icon and the content of window are
different, and it can be easy to distinguish them), low similar (LVS) (same applica- tion type, the icon is the same, and the title characters are partial same, the content is different), high similar (HVS) (same application type, the same icon and most of the title characters are similar, the content of window is also similar) (Figure5.7).
(1) (2) (3) (4)
Figure 5.7: The visual similarity between (1) and (2) isNVS, (1) and (3) is LVSand (1) and (4) is HVS.
5.4.2 Hypothesis
H1 The stack scanning is expected to reduce the search and selection time compared to other techniques, especially when the number of windows is high and the visual similarity is important because the other techniques may fail due to the difficulty to identify a specific window (Expos´e, Taskbar, Alt+Tab, Direct pointing) or when the number of keystrokes becomes too high (Alt+Tab).
5.4.3 Apparatus
The stack scanning technique was implemented in C# on Windows XP/Vista. We used our implementation of an Expos´e clone to perform the experiment in a Windows environment and our implementation is similar to the Apple’s Expos´e (Figure 5.4). We used a PC running Microsoft Windows XP using a 22 inch LCD monitor with a 1680 × 1050 resolution. The mouse is a standard optical one with two buttons and a clickable wheel that can be used as a middle button.
5.4 Experiment
5.4.4 Participants
10 people (6 male, 4 female) with a mean age of 27.4 (SD=2.95) participated. They were recruited from the university (2 civil engineer, 1 mechanic, 1 electronic engineer, 1 chemical engineer and 5 computer scientists) and said they spent at least 8 hours a day working on a Microsoft Windows system. The female participants reported that they mainly usedDirect pointing and Taskbar (with the group by application option disabled) and four reported that they often usedAlt+Tab.
5.4.5 Experimental Design
A repeated measures within-subjects design was used. The independent variables were Technique with 5 levels (Direct pointing, Expos´e, Taskbar, Alt+Tab, stack scan- ning), number of windows Num with 3 levels (8, 12, 16), distribution of window size DisSize with 4 levels (SmallWindow, NormalWindow, LargeWindow, the proportion of three types of window in the total number of windows, 2:1:1 (Size211), 1:2:1 (Size121), 1:1:2 (Size112), 0:0:1 (Size001) (all windows are maximum size, but they have not been maximized, because when the window is maximized, it can be not moved by the mouse)).
For example, when the number of windows is 8, Size211 shows that four of them are SmallWindow, two of them are NormalWindow and other two windows are LargeWin- dow. Amount of overlapping (AmOverlap) with 4 levels (0%, 25%, 50%, 75%).
The visual similarity (VS) between windows with 3 levels (NVS, LVS, HVS). The size of the match set match levels (MLs) with 3 levels (1, 2, or 3), the match level (1, 2, or 3) had a different meaning in each number of windows, With Num equal to 8, the match set is 1, 2, or 4, corresponding to the match levels of 1, 2, and 3. With Num of 12 and 16 match sets of 1, 3, 6 and 1, 4, 8, respectively. TheMLs defines the number of windows in total Num (distractors) is similar with the target window. For example, whenNumis eight,MLs is the third level (match set is four) andVSisHVS, this means that four of eight windows have high similar with the target window.
The two main factors are the window switching techniques (Taskbar, Alt+Tab, Di- rect pointing, Expos´e, stack scanning and the scenario conditions (Num, MLs, DisSize, AmOverlap). The main measure is the completion time and error rate to perform a window switching technique to find the window of interest in different scenarios. Our experiment used real application windows such as Notepad, Word and PDF document as
the target windows. We believed that participants would easily be able to recognize real application windows. In the Taskbar condition, the number of windows on the Taskbar never exceeded a threshold that would cause it to add a second line with a scroll button and the group by application option was disabled.
When the distribution of window size condition isSize001(all windows are maximum size, theoverlapof any window (except the foreground window) is 100%, theAmOverlap condition only has one level (75%), so in order to ensure a balance design, the (Size001) condition will be handled as a separate part of our main experiment, and we analyzed it separately.
The experiment consists of 1620 (Num x VS x MLs x DisSize x AmOverlap x Technique) (Main experiment) + 135 ((Numx VSx MLsxTechnique)) (DisSizeis Size001) (Second experiment) trials. Orders for techniques, number of windows, visual similarity and amount of overlapping conditions were counter-balanced across partici- pants using a balanced Latin-square. For distribution of window size condition, each trial first used the distribution of Size001, order for other three distribution conditions were counter-balanced across participants following a Latin-square. Taking into account the total time for this experiment (over 5 hours), we divided the experiment into three parts by the number of windows.
The performance of some techniques depends on the target window’s position in the stack (Z-order) and the performance difference would be great between different window Z-order in the stack (e.g. forAlt+Tab, the difference can be important between pressing one time Tab key and pressing ten times Tab key to get the target window). So if the target window is randomly presented in the stack, the performance may be very volatile for a small number of trials (4 trials).
To overcome this issue, the target window can be randomly positioned in the stack but the average Z-order for one subgroup trial has to respect the theoretical median Z- order. We used the mean time of that subgroup to compute one successful trial. For example, participants used Alt+Tab to switch windows, the number of windows is 8 and the theoretical average number of pressing the tab keyboard is
P8 i=1
(i−1)/(8−1) = 4 (median Z-order position), the target window is randomly presented in the stack, but the average number of pressing Tab key in one subgroup should be 4 times. Stack scanning also depends on the target window Z-order. So the number of layers is very important