2005 ACM Symposium on Applied Computing
Comparing Table Views for Small Devices Carolyn Watters
Rui Zhang
Jack Duffy
Faculty of Computer Science Dalhousie University Halifax, Nova Scotia, Canada, B3H 1W5 Tel: 1-(902)494-6721
Faculty of Computer Science Dalhousie University Halifax, Nova Scotia, Canada, B3H 1W5 Tel: 1-(902)444-9466
School of Business Administration Dalhousie University Halifax, Nova Scotia, Canada, B3H 1Z5 Tel: 1-(902)494-1838
[email protected]
[email protected]
[email protected]
critical for users of data on small screens and need to be considered carefully for objects that are expected to migrate from device to device, especially when the screen size of the target devices varies significantly. In this paper we examine tables on small screen.
ABSTRACT Users expect access to Web data from a wide range of devices, both wired and wireless. Many users switch back and forth between devices, including laptops and Personal Data Assistants (PDAs), and expect to be able to continue working with that data. The goal of this research is to inform the design of applications that support the user by providing reasonably seamless migration of Web data among internet-compatible devices with minimal loss of effectiveness and efficiency. Earlier studies concentrated on the dynamic transformation of text content, lists, and forms embedded in web pages for access on a range of devices from desktop to handheld. This study focuses on the transformation of large tables onto small mobile devices. In this paper we report on the results of two user studies that examine effectiveness and efficiency of three basic models for the display of tables, originally intended for use on larger screen, on PDA size screens for both simple and complex tasks.
Consider, by way of example, the needs of a user who accesses a large table of data on his or her laptop or desktop and subsequently needs to refer to that data on a PDA or a user who is using a PDA while collaborating with someone who is using the same table on a large screen. We can think of examples in drug interaction tables, for example, where the pharmacist and the physician are better served by common view of the table under discussion. Errors and frustration may result from large variations in the information model for access on different devices. Reorganization of previously seen data increases the user’s cognitive overhead as they need to consciously remap their model of the data and this may increase errors in locating data and in the comprehension of data [1]. To this end, we propose that users would benefit from a consistent mental model of the information that is independent at some level, from the actual device in use so that they can migrate between devices with minimal content and comprehension loss. There may be tradeoffs, however, between the maintenance of a consistent mental model and ease of use on individual devices.
Categories and Subject Descriptors H.5.2 Information Interfaces and Presentation; User Interfaces; Screen Design
General Terms Documentation, Performance, Design, Experimentation, Human Factors.
In this paper, we focus on the display and manipulation of existing tables, such as those found in web pages, on PDAs. That is, we are concerned with the target representation of such tables rather than the complexities of the algorithm used to perform the transformation.
Keywords PDA, small screen, handheld device, auto-transformation, Focus + Context, Tables.
2. RELATED WORK
1. INTRODUCTION
Tables are inherently two-dimensional structures, where the spatial layout is an essential component of the semantics. The contextual component of tables, such as title and column headings, particularly on small screens, has been shown in earlier work to have a significant effect on both effectiveness and efficiency of complex task and on the small screen devices [2].
The rapid acceptance of handheld Internet-compatible devices has increased the requirement to access information efficiently and effectively across a variety devices. Users in many fields, such as business, medicine and education rely on using both full size and small mobile devices for information tasks. Usability factors are
2.1 Display Problems on Small Screen Devices
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Any design for use on the small screen must be informed by consideration of those characteristics that have been shown to affect the performance of users on small screens. An early study by Reseil and Shneiderman [3] showed that the smaller screen size does slow down the reading time, while a study by Jones et al. [4] found that the smaller screen size impedes search task
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browsers NetFront v3.0 [7] and ThunderHawk [8] use similar approaches. The ThunderHawk browser reduces the font and flips the table so that users use the PDA horizontally to view the table. Unfortunately this results in more difficult use of the control buttons of the PDA.
performance. However, Duchnicky and Kolers [5] discovered that even for very small displays of only a few lines of text, the ability of the user to read and understand information was not adversely affected. A study of task performance on tables using small screens by Watters et al [2] confirmed that for both simple and complex tasks the allocation of screen space for context information improved efficiency and effectiveness, where the context used included headings and relative position within the table. Related work has been done on the display and use of large amounts of data on large screens. In particular Focus + Context [6,13] designs have been used with success on regular size screens. Focus + Context designs present a consistent overview of the entire data set, which may be too small to be legible, accompanied by functionality that allows the user to focus in on parts of the data within the context of the overall view. The key constraint for using data in tables on mobile devices is the limitation imposed by the screen size. Using a small screen device to view a table designed for use on a PC or laptop is like trying to use a small window to read the newspaper. Users can only ever see a small part of the text. The challenge is to balance a need for context with a need to display as much data as possible on the screen by tailoring the data presentation and/or navigation functionalities to minimize negative performance effects on both efficiency and effectiveness.
Figure 2. The sample page displayed with IE.
Linear - An other general approach used for PDAs has been to transform tables from their original 2-D structure into a linearized version. AvantGo [9], for example, and OceanLake’s mScope [10] use this technique. The tables are transformed into sublists, with each sublist defined by the cells in one row of the original table. One difficulty for the user with this kind of transformation is providing useful context of the column headers. Each entry of the table may be explicitly associated with its headers as shown in the sample in Figure 3. This makes the context clearer but may be problematic for long headers. Furthermore, the creation of sublists may make certain tasks more difficult. For example, it is reasonably easy to find the number of graduates for a given department in a given year or which year a department had the fewest students but more difficult to find out which department has the most students in a given year.
The example shown in Figure 1 is a web page with an embedded table as seen on the PC screen, where the user can view all the table contents at once.
Figure 1. Sample Web table displayed on PC screen.
Default - The most straightforward method to present tabular data on the small screen is to simply display as much of the source page as fits the screen with scrolling to navigate the rest of the data. The result can be seen in Figure 2, using Internet Explorer (IE). This imposes a burden on the user as they now have to scroll both vertically and horizontally to view the whole table. In addition, unless the headers of the columns and the rows are locked on the screen, it is very easy to get lost or disoriented. The
Figure 3. The sample page displayed with IE.
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Overview - The third technique, reminiscent of the Focus + Context techniques [6,13] used on larger screens, tries to reduce the table so that it fits in its entirety on the PDA screen. The Palmscape [11] browser, for example, keeps the two-dimensional structure of the table by shrinking the column width, word wrapping within the columns, to reduce the width of the table to fit the device screen. Word wrap unfortunately also reduces readability for the user.
functionality to select and create any sub-table they want (Figure 5). A single click enables the user to return to the original table or a previous subtable. If the user wants to add more columns, they can select additional ones from the column header list and these are added into the subtable in a position consistent with their position in the original table.
Microsoft’s Pocket PC version of Excel provides functionalities for spreadsheets that may apply to non-spreadsheet tables, such as Web Tables, as well, including column width adjustment, row and column selection, frozen headings, and spreadsheet overviews.
3. PROTOTYPES For use in our study we developed three prototype systems as examples of three general techniques for the display of tables on small screens; Default View (DV), Linear View (LV), and Overview (OV). Default View Prototype. The Default View simply displays the table without transformations using the Internet Explorer browser, as shown in Figure 2. Linear View Prototype. The Linear algorithm used for this study created sublists, where each sublist contained the data of one row of the original table, as shown in Figure 3. Overview Prototype. The Overview prototype, written in Java, mimics features found in the Pocket PC version of Excel. The font size of the data in the new table is reduced and zoom-in and zoom-out functionalities are provided for the user. The table columns are ellipsed to fit the width of the small screen. This works reasonably well up to 11 columns and 15 rows on our Toshiba PDA. If the table is bigger than 11X15, the table appears to be largely empty. The user can adjust column widths as needed and mouse-over pop-ups can be used to show the contents of individual cells, as seen in Figure 4a&b.
Figure 5. Subtable Selection.
4. USER STUDY AND RESULTS Two user studies were conducted to compare efficiency and accuracy of the prototype Overview (OV) model with Default (DV) and Linear (LV) Views on the PDA. We measured efficiency by counting the number of actions, button clicks or scrolling actions, taken by the user to complete each task. We measured accuracy as the number of correct results of each task. In addition, we surveyed the users to determine their perception of ease of use and preferences they may have. Both studies used the same sample data table, shown in Figure 1, and the three prototype systems discussed in Section 2. Task Types. Five task types were defined to represent a range of table oriented tasks, from simple lookups to more complex comparison tasks. For each task type we used a set of three questions randomly assigned to users. Sample questions are presented in Table 1. Task L1, the simple lookup task, required the user to locate a cell in the upper left corner of the original
Figure 4. Sample page with Overview Prototype and mouse overs. For users who need to concentrate on sections of the table or need to juxtapose data for some task, the prototype provides the
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table. Task L2, also a lookup, required the user to locate a cell in the bottom right of the original table, which would involve navigation through the table in all cases. The complex tasks involved both lookup and comparison activities on the part of the user. The first of these, Task C1, required the user to locate 2 cells in different columns of the original table. Task C2 required the user to locate and scan one column of the original table to identify the minimum value. Task C3 is similar to C2 with the addition of locating and scanning a second column of the original table.
Table 2. Comparison between three techniques Mean number of actions
Task Type
Sample Query
L1
Simple Lookup
In 1994-1995, how many graduates were enrolled in Dentistry?
L2
Scroll Lookup
In 2000-2001 how many graduates were enrolled in Pharmacy?
C1
Complex Grid
For the periods 1995-1996 and 19971998, which had the larger increase: Optometry or Physical therapy?
C2
Complex Column
In 1998-1999, which program had the fewest enrollments?
C3
Complex Compare
In the two periods 1996-1997 and 19992000, which program had the fewest students enrolled?
TaskL2 (SD)
TaskC2 (SD)
DV
3.33 (1.15)
17.00 (9.64)
38.33 (18.77)
LV
3.67 (1.53)
3.67 (2.08)
10.00 (6.24)
OV
4.00 (1.00)
4.33 (3.21)
10.00 (6.56)
Using a Univariate general linear model we found a strong indication that View, Task, and their interaction explain about 75% of the variation in efficiency (R2 of .74), with significant effect of view alone at the p=.002 level, task alone at the p=.001 level and combined view and task at the p=.030 level. Looking at the graph in Figure 6 we can see that DV is dramatically different from both the LV and OV for efficiency. No significant difference, however, was found for efficiency between the LV and the OV for these tasks.
Table 1. Task types and sample queries Task
TaskL1 (SD)
4.1 User Study 1 The first user study had nine participants all familiar with PDAs. The users, in blocks of three participants, where given three tasks; the two lookup tasks and Task C2, the complex column task. In this study we were interested in a general level comparison of the three views across straightforward tasks, without any overt training on any of the systems.
Figure 6. Efficiency Graph for User Study 1. The Student-Newman-Keuls test, used to compare post hoc pairs of group means for View and Task against efficiency, confirmed that Task C2 was more difficult than either of the lookup tasks for the users and that the efficiency for Task C2 was significantly lower than for either Task 2 or Task 1, at the p
