ABSTRACT
Title of Dissertation: ZOOMABLE USER INTERFACES FOR THE AUTHORING
AND DELIVERY OF SLIDE PRESENTATIONS
Lance Everett Good, Doctor of Philosophy, 2003
Dissertation directed by: Professor Benjamin B. Bederson
Department of Computer Science
Millions of slide presentations are being authored and delivered with computer
software every day. Yet much of the computer?s power for these tasks remains untapped.
Existing interaction techniques leave presenters wrestling with limited size computer
displays to get meaningful overviews of their work. Without these overviews, they have
trouble finding patterns in their data and experimenting with alternate organizations.
They also have difficulty communicating the structure of large or complex talks to the
audience and keeping the audience oriented during unexpected transitions between ideas.
A natural solution is Zoomable User Interfaces (ZUIs) since they offer the capability to
view information at multiple levels of detail and smoothly transition between ideas. This
work presents two ZUIs, Niagara and CounterPoint, for authoring and delivering slide
presentations.
Niagara is a ZUI workspace for authoring presentation content with techniques to
improve authoring in the zoomable environment. Empirical evaluations of ZUI-based
authoring tools revealed performance improvements and subjective preferences over
folder-based interfaces for organization tasks. Users were 30% faster with ZUIs than
with folders in completing a simplified shape organization task. Some classes of users
were also faster with ZUIs than with folders in completing a text-based organization task.
Users performing both tasks exhibited a strong preference for ZUIs over folders.
CounterPoint provides a number of features to simplify the creation and delivery
of ZUI presentations. The effects of these presentations on the audience were evaluated
in a controlled comparison of presentations with slides only, slides with spatial layouts,
and slides with spatial layouts and animation. The study revealed a strong subjective
preference and higher ratings of organization for presentations with spatial layout.
Feedback was also gathered from presenters who used CounterPoint to deliver
over 100 real-world presentations. They indicated that CounterPoint helped them
communicate overviews and multi-level presentation structures. More experienced
CounterPoint presenters also found that CounterPoint helped them keep the audience
oriented when navigating the presentation in response to audience feedback.
ZOOMABLE USER INTERFACES FOR THE AUTHORING AND DELIVERY OF
SLIDE PRESENTATIONS
by
Lance Everett Good
Dissertation submitted to the Facultyofthe Graduate Schoolofthe
University of Maryland, College Park in partial fulfillment
oftherequirementsforthedegreeof
Doctor ofPhilosophy
2003
Advisory Committee:
Professor Benjamin B. Bederson, Chair
Professor Doug Oard
Professor AdamPorter
Professor Ben Shneiderman
Dr.MarkJ.Stefik
? Copyright by
Lance Everett Good
2003
ii
DEDICATION
To Cristie
iii
ACKNOWLEDGEMENTS
I had the opportunity to work with two great advisors. Ben Bederson was my
academic advisor and always gave me extremely helpful advice and direction. Ben's
creativity and pragmatism inspired and encouraged me. I also appreciated Ben's intellect
and hope I can take away some of his great reasoning skills. My unofficial second
advisor was Mark Stefik. I benefited immensely from the experiences and wisdom that
Mark shared with me. Mark always challenged me with his highly scientific approach to
problems and motivated me with his excitement for new ideas. I also have to thank Mark
for going out ofhis wayto allow me to work at PARC.
Thanks to Doug Oard, Adam Porter, and Ben Shneiderman for agreeing to be on
my committee. They also gave helpful corrections and advice that improved this
dissertation. Thanks also to Evan Golub and Bill Killam for letting me use their HCI
classes in my audience user study.
I would also like to thank the members of the labs I worked in. I really enjoyed
the cooperative spirit in the Human-Computer Interaction Lab and appreciated all the
knowledge and kindness shared with me by the HCIL's students, staff, and faculty.
Thanks especially to Juan Pablo Hourcade for his help throughout my time at Maryland.
Thanks also to Allision Druin, Jesse Grosjean, Harry Hochheiser, Hilary Browne
iv
Hutchison, Hyunmo Kang, Jaime Montemayor, Catherine Plaisant, Anne Rose, and
Bongwon Suh. Thanks to Allison Farber for designing artwork for CounterPoint.
I have also appreciated my time in the Information Sciences and Technology Lab
at PARC. I have enjoyed getting to know the members of ISTL and have benefited from
the diverse academic backgrounds in the lab. I especially appreciated the willingness of
lab members to participate in my (often-tedious) user studies. A special thanks to the
past and present members of my area, Patrick Baudisch, Eric Bier, Sascha Brawer,
Nathan Good, Bill Janssen, Paula Newman, Ken Pier, Kris Popat, and Polle Zellweger,
for their advice and feedback throughout my time at PARC. The members of UIR, Stu
Card, Ed Chi, Julie Heiser, Lichan Hong, Jock Mackinlay, Peter Pirolli, and Christiaan
Royer also gave me good advice on statistics, user studies, and how I should present my
work. Of course, I also have to thank my office neighbors Jeanette Figueroa and Mali
Sarpangal for their friendliness and administrative support.
Thanks to my friends Mike and Melissa Fuller, Joshua and Alicia Ackerson, and
Lori Walker for their support and being willing to be guinea pigs in my studies. I am
grateful for the support of all my other friends, especially Chris Brown, Trey Jackson,
Jim Lin, Ted Hseih, Rich Hsu, Dave and Caroline Seinknicht, Jonathan Berent, Timothy
Landhuis, Sandra Park, Hiro Tsuda, Margaret Venables, and Greg and Karin Jackson,
who helped me keep my sanity during my time as a student.
I am also especially grateful to my family. Thanks to my parents, Ron and Lori
Good, for instilling in me the value of education. My sister, Rhonda Good, was also a
source of encouragement. I also send a shout out to my brother-in-law, Mike Kelly -
maybe there is still hope for us Aspergers. Also my baby, Ainsley (the
v
cutest...baby...ever), always had a big smile to cheer me up when I got home from work.
And finally, I could never acknowledge everything my wife Cristie has done to make this
dissertation possible. She supported me in countless ways and sacrificed herself in taking
care of Ainsley in the final months of my writing. Cristie also suffered through a barrage
of pilot studies and gave me many good ideas for improving the usability of my software.
vi
TABLE OF CONTENTS
LIST OF TABLES........................................................................................................ ix
LIST OF FIGURES.......................................................................................................x
Chapter 1: Introduction.................................................................................................1
1.1 What?s so hard about authoring a presentation?...............................................2
1.2 What?s so hard about delivering presentations?................................................8
1.3 Finding Solutions Based on Zoomable User Interfaces..................................11
1.3.1 Authoring Presentation Content.............................................................12
1.3.2 Delivering Presentations........................................................................15
1.4 Niagara..........................................................................................................18
1.5 CounterPoint .................................................................................................19
1.6 Contents........................................................................................................20
Chapter 2: Authoring Presentations and Niagara.........................................................22
2.1 Related Work................................................................................................22
2.1.1 Authoring Systems and Techniques .......................................................22
2.1.2 Evaluations............................................................................................28
2.2 Niagara..........................................................................................................37
2.3 First Design Iteration - Niagara version 1 ......................................................38
2.3.1 Study.....................................................................................................40
2.4 Second Design Iteration - Niagara version 2..................................................41
2.4.1 Study.....................................................................................................42
2.5 Third Design Iteration - Niagara version 3.....................................................50
2.6 Studying a Simplified Task............................................................................54
2.6.1 The Core Question: Zooming versus Folder Interfaces..........................57
2.6.2 Scaling Effects and Other Parameters of Task Difficulty........................68
2.7 Extending Simplified Task Results to Text....................................................79
2.7.1 Method..................................................................................................81
2.7.2 Results and Discussion ..........................................................................86
2.7.3 Revisited: Scaling Effects and Other Parameters of Task Difficulty.......94
2.8 Niagara in Use...............................................................................................97
Chapter 3: Niagara Features and Implementation........................................................98
3.1 Unique Features in Niagara version 1............................................................98
vii
3.1.1 Objects and Interactions.........................................................................98
3.1.2 City Lights........................................................................................... 103
3.2 Unique Features in Niagara version 2.......................................................... 109
3.2.1 Spatial Overview Features................................................................... 109
3.3 Unique Features in Niagara version 3.......................................................... 114
3.3.1 Similarity Matching............................................................................. 114
3.3.2 Overview and Group Representations.................................................. 115
3.3.3 Zooming.............................................................................................. 118
3.4 Implementation Details................................................................................ 120
3.4.1 Bumping.............................................................................................. 120
3.4.2 City Lights........................................................................................... 122
3.4.3 Automatic Text Reduction................................................................... 123
Chapter 4: Presentation Delivery and CounterPoint................................................... 129
4.1 Related Work.............................................................................................. 129
4.1.1 Navigating Presentations...................................................................... 129
4.1.2 Navigating in ZUIs.............................................................................. 132
4.1.3 ZUI Narratives..................................................................................... 133
4.2 CounterPoint ............................................................................................... 134
4.2.1 Early Mockups..................................................................................... 135
4.2.2 A Functional Prototype........................................................................ 137
4.3 The Audience? Perspective.......................................................................... 140
4.3.1 Potential Cognitive Benefits................................................................. 141
4.3.2 An Audience Study.............................................................................. 148
4.4 The Presenter?s Perspective......................................................................... 156
4.4.1 Enabling CounterPoint Features........................................................... 157
4.4.2 Real World Experience........................................................................ 161
Chapter 5: CounterPoint Features and Implementation.............................................. 165
5.1 Implementation ........................................................................................... 165
5.1.1 Architecture......................................................................................... 165
5.1.2 Rendering............................................................................................ 167
5.2 Features....................................................................................................... 168
5.2.1 Layout................................................................................................. 168
5.2.2 Path..................................................................................................... 170
5.2.3 Presentation Delivery........................................................................... 173
Chapter 6: Future Work ............................................................................................ 176
6.1 Authoring Presentation Content................................................................... 176
6.1.1 Niagara Implementation....................................................................... 176
6.1.2 Evaluation ........................................................................................... 177
6.2 Presentation Delivery .................................................................................. 178
6.2.1 CounterPoint Implementation.............................................................. 178
6.2.2 Evaluation ........................................................................................... 182
Chapter 7: Conclusion............................................................................................... 184
viii
7.1 Presentation Authoring................................................................................ 185
7.2 Presentation Delivery .................................................................................. 188
7.3 Contributions............................................................................................... 189
Appendix A: Questionnaires..................................................................................... 191
A.1 Niagara Qualitative Study Questionnaire..................................................... 191
A.2 Niagara Quantitative Shape Study Questionnaire......................................... 192
A.3 Niagara Quantitative Text Study Questionnaire........................................... 193
A.4 CounterPoint Audience Questionnaire......................................................... 193
References.................................................................................................................. 195
ix
LIST OF TABLES
Table 1: A description of the operations coded in the video..........................................44
Table 2: The means and standard deviations of dependent variables measured in the study.
..............................................................................................................................46
Table 3: Tradeoffs the organizer makes in using spatial clusters vs. formal collections.. 49
Table 4: The means and standard deviations of dependent variables measured in the study.
..............................................................................................................................67
Table 5: The means and standard deviations of dependent variables measured in the study,
divided on the single within-subjects factor of tool type. .......................................88
Table 6: The means and standard deviations of dependent variables measured in the study,
divided on the within-subjects factor of tool type and the between-subjects factor of
labeling strategy....................................................................................................89
Table 7: Conditions explored in the study. Animation and no spatial layout were not
explored since the animations were not expected to contribute to non-spatial
presentations....................................................................................................... 149
Table 8: The means and standard deviations for the dependent variables measured in the
study................................................................................................................... 152
x
LIST OF FIGURES
Figure 1: A screenshot of Niagara being used to author parts of this document..............19
Figure 2: A screenshot of a CounterPoint presentation on PhotoMesa [i16]...................20
Figure 3: A screenshot of the first version of Niagara with text objects and collections.39
Figure 4: A screenshot of populated workspace in the second version of Niagara. A
spatial overview is available in the top right corner of the workspace....................42
Figure 5: A pilot subject organizing the driving facts using sticky notes on a whiteboard.
..............................................................................................................................44
Figure 6: An example of automatic text reduction applied to an object at several different
zoom levels...........................................................................................................52
Figure 7: A screenshot of automatically created groups in Niagara................................53
Figure 8: A screenshot of the full text tooltips provided in Niagara...............................53
Figure 9: An example shape grouping task. One group has grouping criteria of squares
with blue borders. The second group has grouping criteria of diagonal striped
textures and thin borders. ......................................................................................56
Figure 10: The starting state for the two interface conditions in the shape study. The
folder interface is on the left and the ZUI interface is on the right..........................61
Figure 11: A graph comparing the mean completion times for zooming versus folders.
The error bars indicate standard deviation. ............................................................66
Figure 12: Graph of trials that varied in number of objects............................................72
Figure 13: Graph of trials that varied in the number of groups.......................................73
Figure 14: Graph of trials that varied in the number of group membership criteria. .......74
Figure 15: Graph of trials that varied in the number of overlapping membership criteria.
..............................................................................................................................75
xi
Figure 16: Graph of trials that varied in the total number of shape attributes. ................76
Figure 17: Graph of trials that varied in the number of possible values per attribute......77
Figure 18: The starting state for the two interface conditions in the text-based study. The
folder interface is on the left and the ZUI interface is on the right..........................83
Figure 19: Graphs of the two measures on which interface type had a significant effect.
Thegraphontheleftshowsthesubjectiverankingsforthequestionnaireitem
?What is your overall reaction to using this tool for the task?? The graph on the
right shows the number of groups labeled in each condition..................................87
Figure 20: Graphs of the differences between four measures for each of the 3 labeling
strategies. The top left graph depicts completion time. The bottom left graph
depicts subjective duration assessment. The top right graph depicts subjective
satisfaction. The bottom right depicts group quality. Positive values represent
higher values in the zooming condition, while negative values represent higher
values in the folder condition.................................................................................91
Figure 21: A graph of the number of navigation operations between the different labeling
strategies. The difference between strategies was not significant..........................92
Figure 22: A screenshot of the three object types in the first version of Niagara. The
objects from left to right are atext object, a list, and a subspace............................99
Figure 23: A screenshot of an item being added to a list. The list provides a preview of
what the list will look like when the object is inserted...........................................99
Figure 24: A screenshot of a list shown in three different levels of nested subspaces. The
scale of objects is gradually reduced as the nesting depth of the subspaces increases.
............................................................................................................................ 100
Figure 25: A screenshot of an item being moved over nested subspaces. The item
reduces in size to preview the size it will take when dropped in the space............ 101
Figure 26: A screenshot of a text object being dropped between two text boxes. When
the text object is dropped it ?bumps? other objects out of the way. Prior to being
dropped the text object is in ?flying? mode.......................................................... 102
Figure 27: The magenta object is used as a tool to make space between the green objects.
............................................................................................................................ 102
Figure 28: A simplified portrayal of a City Lights technique....................................... 104
Figure 29: Nested Niagara subspaces with City Lights at multiple levels..................... 105
Figure 30: The first in a sequence of screenshots showing Niagara workspaces with 13
off-screen objects. To show these off-screen objects, overview windows are
xii
displayed on top of the primary workspace windows. The overview windows show
the view rectangle in black and the unseen objects outside this view. These two
screenshots show two versions of City Lights along the larger windows? border
using an orthogonal projection. The left screenshot shows a line projection of the
object bounds and the right screenshot shows a point projection of the object centers.
............................................................................................................................ 106
Figure 31: Two versions of City Lights are shown along the larger windows? border. The
left screenshot shows an orthogonal projection of object center points and the right
screenshot shows a radial projection of object center points................................. 107
Figure 32: Two versions of City Lights are shown along the larger windows? border. The
left screenshot shows a radial point projection of object centers and the right
screenshot shows ?halos? centered on the objects? centers................................... 108
Figure 33: Two versions of City Lights are shown along the larger windows' border that
use a radial projection of object center points. The left screenshot uses a binary
color system with near objects in darker green and far objects in lighter green. The
right screenshot makes the difference clearer by using blue for near objects and red
for far objects...................................................................................................... 109
Figure 34: This figure shows three versions of the spatial overview. The leftmost version
shows a standard geometrically reduced representation of the entire space. The
center version shows an overview with semantic zooming on collection titles. The
rightmost version shows an abstracted version of the objects in the workspace.... 110
Figure 35: A screenshot of an interactive spatial overview. When the user pauses while
dragging an itemonto a subspace in the overview, the systempops up a window to
show an overview of the current subspace........................................................... 111
Figure 36: The overview switches to show the currently selected workspace............... 112
Figure 37: A screenshot of a prototype overview developed for use with the Focus Plus
Context display. .................................................................................................. 113
Figure 38: A screenshot of similarity matching cues shown in red. The user can request
these cues when moving an item in the workspace or dragging in a new item from
one of Niagara?s information sources.................................................................. 115
Figure 39: In the structural/folder view, the system represents automatically created
groups with a label and an iconic representation of the group?s spatial layout...... 116
Figure 40: One implemented version of automatic grouping that changed object colors
but did not display a title bar or rectangular outline............................................. 117
Figure 41: Discrete movements must be interpolated to obtain the ideal bumping behavior.
In the movement shown above, the red object does not bump the green object unless
the movements are interpolated........................................................................... 121
xiii
Figure 42: The red rectangle in both drawings represents the viewed space and the outer
rectangle represents the populated space. The shaded areas represent the set of
objects projected onto the window borders by an orthogonal projection. As the ratio
of populated space to viewed space increases the percentage of the objects in the
orthogonal projection?s blind spots also increases, as can be seen by the growing
corner rectangles above....................................................................................... 123
Figure 43: A graph of text reduction in terms of font size and text length. The black dots
indicate the states represented in Figure 6. The curves indicate objects of the same
size...................................................................................................................... 126
Figure 44: Two text boxes with equal area and equal font size. The right text box holds
less text because its height is between multiples of the font height....................... 127
Figure 45: A mockup of a hierarchical editor for authoring textual presentation content.
............................................................................................................................ 136
Figure 46: A mockup of a path editor for authoring paths through the content hierarchy.
............................................................................................................................ 137
Figure 47: A screenshot of the CounterPoint interface in the hierarchical layout editing
mode for a presentation on Automatic Text Reduction [i143].............................. 139
Figure 48: An example of a concept map taken from Robinson et al. [i195]................ 144
Figure 49: A top level overview for the conditions in the study. The left image is the
overview in the no spatial layout condition. The right image is the overview for
both of the spatial layout conditions. ................................................................... 150
Figure 50: The graph on the left compares the means and standard deviations for
responses to the questionnaire item?Rate how much you liked the visualportion of
the presentation?? The graph on the right compares the means and standard
deviations for responses to the questionnaire item ?How would you rate the
presentation?s organization?? .............................................................................. 153
Figure 51: The graph on the left shows the short and long term recall of outline content.
The graph on the right shows the short and long term recall of outline structure.
These results indicate a possible pattern in the data but theywere not significant.154
Figure 52: A graph of short and long term recall of specific content questions. These
results indicate a possible pattern in the data but were not significant.................. 154
Figure 53: A version of Mark Stefik?s lab overview presentation in CounterPoint....... 164
Figure 54: CounterPoint in layout organizer mode. This allows the author to create text
labels and define a hierarchy with the slides........................................................ 169
xiv
Figure 55: CounterPoint in path editing mode. The panel on the left represents the
sequence of slides and views in the current presentation path. ............................. 171
Figure 56: CounterPoint?s 2D path editor that mimics PowerPoint?s slide sorter......... 172
Figure 57: A screen shot of CounterPoint in presentation mode. Here, the presenter can
alter pre-scripted traversals using various presentation-time interactions. Black
borders indicate slides already visited during the presentation. The current focus is
highlighted in red................................................................................................ 173
Figure 58: An example interface for hierarchically organizing slides........................... 180
Figure 59: An example automatic layout with nested rectangular groups..................... 181
Figure 60: An example of an automatic layout with a network format......................... 181
0
1
Chapter 1:
Introduction
Formal face-to-face communications tend to be accompanied by visual aids when
ideas can be more efficiently conveyed through visuals than by spoken word. The most
common type of visual aid employed in these situations is the slide show. Slide shows
are used in almost every possible setting including the military, businesses, homes,
religious institutions, and educational settings [40, 58]. Onespecificslideshow
presentation tool, PowerPoint, is reportedly being used to author or deliver over 30
million presentations per day [82]. These numbers are only likely to increase as the
volume of human knowledge increases.
Before personal computers became ubiquitous, slide shows were typically
delivered using 35mm slides or overhead transparencies. These media dictated that a
slide show consist of a sequence of fixed images. Although the slides could be reordered,
the decision about which slides to use and the order in which they would appear was
established prior to giving a presentation. Conventions evolved to help keep the audience
oriented during a slide show, such as placing ?outline? slides in a long presentation. A
division of labor also emerged for the preparation of slides. The presenter determined the
content of the slides, but a graphic artist with specialized skills prepared the actual slides.
Computer tools, such as PowerPoint [89], have revolutionized slide show
processes. These tools have made creating highly polished presentation slides possible
for nearly all computer users. Computer-based presentation tools were originally
designed as a substitute for the services provided by a graphic artist. As a result, much of
the functionality provided by these tools is focused on graphic design. This includes
2
controls for visual properties such as layout, font sizes, number of bullets per slide,
colors, etc. As PCs, laptops, and video projectors have become more common these tools
have subsequently been adapted from the graphic design task to the additional tasks of
preparing presentation content and actually delivering the presentation.
Given that the entire presentation process, from conception to delivery, has
moved to a virtual medium, the question arises as to whether computer-based
presentations could be improved now that they are not limited by the constraints of
physical slides. Physical media require that the presentation be divided into a linear
sequence of static fixed-size images. In contrast, computer-based presentations are
potentially free from all of these limitations offering non-linear, dynamic, variable-sized
visualizations. How could these computer tools help a presenter create and organize their
ideas? Could more powerful organizing structures be used than a linear sequence? What
capabilities could help the speaker or audience understand the ideas better and stay
oriented during a presentation? Could slide shows be made more reusable, adaptable in
real-time to the requirements of an audience?
1.1 What?s so hard about authoring a presentation?
Besides the work required for the graphic design of presentation slides, there is
work to be done in preparing the content itself. In fact, authoring presentation content is
cognitively demanding work. The presenter ultimately has to decide what information
will be included in the presentation, how that information will be organized, and how to
best turn that organization into a story for the target audience.
The processes involved in authoring texts, such as presentation content, have been
the subject of much research in recent years. There is some evidence to suggest that
3
these processes differ from person to person [14]. In fact, given the difficulty and
cognitive complexity of the task, it would be surprising if this were not the case. Yet,
several components of these authoring processes have been identified as common across
many authors. Hunter and Begoray [55] analyzed several models of authoring text and
identified four common components in these models including: generating, organizing,
composing, and revising. The generating component is where ideas are collected and
recorded. These ideas can come from the author?s introspection or from external sources.
Next, the organizing component involves making decisions about abstractions and
ordering leading to hierarchical and linear structures. In the composing stage, the author
takes the structure developed in the generating and organization stages and turns it into an
actual usable product. For presentations, this typically means creating the actual
presentation media, such as slides. Lastly, the revision stage involves reviewing the
work, adding new ideas and fixing inconsistencies with the original organization. The
model implies a loose linear ordering to the four activities. However, this ordering is not
strict and is quite likely to be violated. Indeed, one of the models cited by Hunter and
Begoray is quite adamant that these processes are not sequentially completed [49].
The research cited above is not specific to authoring presentation content. In fact,
it is largely targeted at ?writing? tasks that result in some form of printed publication.
Yet even this type of writing is extremely diverse, including many different media,
genres, and formats. As a result, a fundamental assumption in this previous research is
that all text authoring tasks involve essentially the same processes. Perhaps the most
compelling reason why the processes are the same for these diverse authoring tasks
comes from Flower and Hayes who suggest three constraints faced by writers across
4
writing tasks [32]. First, an author is constrained by knowledge because it must be
converted from mental models and distributed sources to a single coherent whole.
Second, written speech compels the author to obey various written language conventions
that often are not followed in the author?s internal dialog or external spoken language.
Lastly, the rhetorical problem constrains the author to thinking about the intended
audience and the conventions of the presentation format. Naturally, these three
constraints have varying importance at different stages of different authoring tasks. In
the specific case of authoring presentations, all three constraints must be satisfied, though
the written speech constraint tends to have lower emphasis since presentation slides often
contain sentence fragments and limited amounts of text. Nevertheless, because these
same constraints arise for a wide range of authoring tasks, the processes needed to solve
these tasks are also similar.
One of the common themes in the early stages of authoring text is the ability to try
many alternatives. This is particularly evident in brainstorming or idea generation, where
more ideas are often created than are actually used in the final product [33, 104].
Exploring alternatives is also important in the organization phase, since many
organizations are typically considered before one is ultimately chosen. In fact, Neuwirth,
et al. present evidence that the number of different perspectives considered in exploring a
set of concepts influences the quality and creativity of the resulting document [76]. As a
result, it is important for the author to be able to switch between many alternative
organizations as the presentation becomes more complete. The more difficult it is to
explore alternatives, the fewer alternatives the presenter is likely to consider.
5
The presenter?s ability to explore alternatives is largely influenced by the
formality of an authoring tool. Unnecessary formality occurs when the representations
and structures of the tool do not match the users' tasks [101]. Mismatches between the
task and the tool increase the costs to the user in converting from their mental
representations to the system?s representations. In addition, formalstructures can
introduce modification costs that reduce the chances that an author will explore
alternative organizations. Some of the most well known examples of this type of
formality mismatch were noted in the context of a textual authoring tool called Notecards
whereuserssometimeshadtointerrupttheirworktothinkofanameforanoteorwhere
toputanote[46]. A similar presentation-specific example occurs in many slide
authoring tools when a presenter is entering ideas and a slide becomes full. At this point,
the presenter is forced to make a decision to either reorganize the slide or move to a new
slide to continue at the point where the fault occurred. In this case, the presenter's goal is
simply to enter the information but the tool imposes a formal division between slides to
which the presenter must adapt.
Beyond simply exploring alternatives, Flower and Hayes describe text authoring
in terms of finding solutions to an "excessive number" of constraints [32]. Because this
authoring process requires the author to make choices from such a large constraint space,
it is cognitively demanding work. An author?s grasp on the large quantity of information
and large number of constraints can be extremely delicate.
As an author works, many ideas about the presentation's content and structure
pass through the author's short-term memory. If interruptions arise, these fleeting ideas
can be temporarily lost or even permanently forgotten. These interruptions can take
6
several forms. One of the most common interruptions in slide authoring software is
similar to what Dertouzos calls the perfection fault [25]. The perfection fault occurs
whenever an author becomes involved in manipulating aspects of the visual appearance
of a document beyond what is necessary or beneficial to the authoring task. This type of
interruption has been reported to plague slide authoring software to such an extent that
slide authors often neglect their content in favor of visual appearance [39, 40, 113].
Focusing on graphic design in this way provides users with a false sense of preparedness.
Because the presentation looks good, authors are often lulled into thinking the content is
better than it is. Similarly, presenters tend to ignore the subtleties and complex
relationships of difficult concepts, choosing instead to simplify ideas to one line phrases
that conform to the bulleted lists provided by many slide authoring tools [82, 113].
Perhaps more cognitively harmful is that the consideration for the visual design of
presentation slides can introduce additional constraints to an already cognitively
demanding task. It also compounds the amount of effort needed to maintain a set of
slides as the visual appearance must often be adjusted as the content grows and changes.
Another type of interruption can arise from the low-level interactions with a
computer based authoring tool. Human factors researchers have used GOMS (Goals
Operators, Methods, and Selection) models or other keystroke level models to
characterize these types of interactions (for example, see the work of Card, et al. [16]).
Some typical operations considered in low-level models of computer-based tools include
pointing, clicking, keystrokes, hand movements, mental preparation, etc. These
operations become distractions when they occur too frequently, take too much time to
complete, or require too much attention or concentration. Here they can displace the
7
author's thoughts about content with thoughts about how to accomplish certain sub-tasks
with the tools provided. One example specific to authoring presentation content arises in
the low-level interactions involved in comparing objects on a computer display.
Comparisons are extremely common in brainstorming and organizing presentation
content as authors look for patterns and consider alternative groupings and orderings.
Presentation authors are frequentlyscanning their information for patterns and looking
for the best way to structure and order that information. In a large workspace, like a
whiteboard or a table, an author can spread the information out and do comparisons
between objects with eye and head movements. On a computer screen this same task
often requires the author to scroll or move between slides or folders in order to see the
objects to be compared. The result is that the author often must trade fast eye movements
for slower hand movements, making the task much more time consuming and thus
cognitively disruptive.
This previous example hints at one of the overarching difficulties in authoring text
on a computer display, namely insufficient screen space. Computer displays are typically
30 to 40 times smaller than ordinary tables or whiteboards [51]. Research suggests that in
many situations the limited view of the computer display can make both writing and
reading tasks more difficult than with traditional physical media because of a lack of
good global awareness in the text [35, 72, 97]. Sharples presents a table comparing the
properties of several media used for authoring textual content [98]. The table reflects that
computers have many characteristics missing in other media but typically lack the facility
for providing good overviews of textual content. This limitation is noted more generally
by Henderson et al. [51]. They describe the similarities between the space management
8
strategies employed on a computer display and in computer memory. Both situations can
lead to thrashing, where more time is spent managing space than doing work if additional
mechanisms are not introduced. Although this situation will be mitigated by the
availability of larger computer displays, it is likely to remain an important issue for
portable devices such as laptops and PDAs.
1.2 What?s so hard about delivering presentations?
The art of presentation is to arrange ideas in a way that is understandable and
holds the audience?s interest. If there are only a few things to say, we can often just make
a short speech rather than bothering with a slide show. Slide shows are most useful when
there is too much to just ?say?, and when we want to reinforce the audience?s
understanding by combining visual images with spoken words. Yet even slide shows can
provide inadequate support for many presentations.
One place where slide shows are often inadequate is in helping the presenter
communicate the structure of a presentation to the audience. A slide show does not
inherently provide any visual support to the presenter in explaining how the individual
slides relate to one another nor how they fit into the larger structure of the presentation.
Presenters have adapted to this limitation by using outline or overview slides. Outline
slides are typically used to layout the structure of the presentation to help the presenter
communicate relationship between topics. However, presenters tend to use these
overview slides sparingly. This is due in part to the extra effort required to create these
slides and keep them up to date as the presentation is reorganized. Presenters also tend to
feel that including too many overviews can seem pedantic, as suggested by the extreme
case in which an overview slide is shown after each individual content slide. Perhaps the
9
most limiting aspect of outline slides is that a single outline slide often can't convey the
structures of large presentations or presentations with multiple levels of detail. Multiple
outline slides can be used but the audience is forced to mentally integrate the
disconnected structures. More often than not in these situations, presenters rely on their
verbal descriptions of the presentation structure or develop ad hoc visual designs.
Having an insufficient portrayal of the structure can be particularly disruptive
when a presentation is very interactive, such as a business or military briefing. In these
cases, the audience will often interrupt the presenter at inappropriate times to ask
questions that would have been more appropriately asked at a previous point or that will
be covered later in the presentation. Similarly, the presenter will often have more
information than can be covered in the available time. Yet, the audience is often unaware
of the additional ?backup? information on a topic so they may hesitate to ask questions
when more details are readily available.
Slide shows are also inadequate in supporting the presenter when unexpected
things happen during a presentation. There are often last minute changes in the amount
of time available for a presentation or in who will be in the audience. As mentioned, it is
also common for the audience to ask questions about topics that were already covered,
that will be covered later, or that are discussed in backup slides that are not included in
the main presentation. When these situations occur, the presenter is likely to need to
navigate between nonconsecutive slides in the presentation. During navigation in a
traditional slide show, the transitions between slides typically convey no information
about the underlying content. Even worse, these transitions are often filled with spurious
graphical animations that can distract the audience from the presentation content. The
10
result is that these traditional slide transitions do not help communicate the conceptual
transition and the presenter is left to provide the context for the topic shift. For a well-
scripted presentation, the presenter can anticipate the limitation and account for it by
inserting explanatory transition dialog. However, when unexpected navigations occur
during the presentation, the presenter must improvise a transition to describe how two
slides relate or how the next slide fits into the larger presentation structure.
Improvising to meet the demands of changing time constraints or audience
feedback puts a lot of stress on the presenter. The presenter is typically thinking of what
to say while simultaneously trying to navigate and manage a slide show. This kind of
multitasking conflicts with a fundamental limitation in human short-term memory that
makes it difficult to speak and simultaneously attend to other cognitive tasks [106]. As a
result, the cognitive overhead of slide show navigation should ideally be minimized to
allow presenters to focus on what they are going to say. Yet in reality, navigating a
traditional slide show is often unnecessarily demanding because the slides are agnostic of
the structure and content ofthe presentation. To jump out ofthe scripted presentation
order, a presenter must perform an unaided linear search to find the particular idea or
slide of interest.
In addition, the interaction techniques used to search are often problematic. In the
days of physical slides or transparencies, the linear search meant flipping through slides
in a carousel or through transparencies in a pile. In software presentation tools, this
search often means scrolling through linear lists of slide titles, grids of thumbnails, or the
actual full screen slides. These scrolling interfaces typically make use of traditional GUI
widgets that were designed primarily for use at an individual workstation (for example,
11
see discussion in [75]). As a result, they can be inappropriate in the presentation setting
as they require a high level of attention and pointing precision. They can also be visually
distracting and unprofessional since they do not follow the same design constraints as the
rest of the presentation.
One technique used to facilitate impromptu navigations in software slide shows is
hyperlinking. Hyperlinks reduce the amount of attention and precision needed for
navigation and eliminate the need for a linear search. Hyperlinks can also be designed to
visually conform to the rest of the presentation. However, hyperlinks are frequently not
created because presenters are typically unable and unwilling to anticipate all the points
at which they may want to deviate from the primary storyline.
1.3 Finding Solutions Based on Zoomable User Interfaces
Most of the problems described above for both authoring and delivering
presentations become more evident as the amount of information increases. The more
information a presentation contains the more work there can be in organizing it. When
there are a lot of ideas, there are more supporting visuals that the presenter needs to
access during the talk. In both authoring and delivery, the presenter is concerned with
distinguishing the main points from the details and finding the best story to communicate
the important ideas to the audience.
One of the constants in presentations is that information is delivered in chunks.
For example, there is only so much information a video projector can display at once.
The combination of large amounts of information, a need for structure, and limited space
is at the root of many of the difficulties in preparing and delivering information. It is also
12
a good match for Zoomable User Interfaces since these interfaces directly address the
problem of handling variable amounts of information.
This work proposes techniques based on Zoomable User Interfaces (ZUIs) as
solutions to the problems described above. ZUIs are an alternative to traditional
techniques for visualizing information. ZUIs display information on a conceptually
infinite two-dimensional plane. They allow users to change their view of this plane
through panning and zooming to access more information than can typically be displayed
on a single screen. A fundamental characteristic of zooming and panning operations in
ZUIs is that they are animated. These types of animations give a sense of physical
movement by mimicking such physical acts as sliding a paper on a table (panning),
looking at a paper more closely for detail (zooming in), or holding a paper at a distance
for more context (zooming out). ZUIs have been used in such settings as visualizing
histories [52], authoring and presenting children's stories [13, 30], traversing file system
hierarchies [9], and image browsing [6].
1.3.1 Authoring Presentation Content
ZUIs are fundamentally based on 2D workspaces. 2D workspaces such as
whiteboards, blackboards, and tabletops have long been recognized for their beneficial
qualities for brainstorming and organizing ideas. Many of these same qualities have been
demonstrated for organization tasks with 2D workspaces on computer displays. One
important quality for an organization task is that these workspaces exploit human
memory for spatial location. In a computer-based organization task, spatial organizations
have been shown to improve both recall speed and recall error rates over non-spatial
organizations [93].
13
2D workspaces also make no limitations on where you can move objects or how
objects have to be aligned. This allows authors to indicate subtleties of information
relationships such as positioning objects midway between two related groups of objects
or positioning objects to indicate uncertainty in their order. The ability to portray subtle
or ambiguous relationships frees the author from having to make premature decisions
about how objects are structured. As a result, the interface has an informal quality that
encourages authors to explore alternative relationships in their data to find the best
organizations.
Because ZUIs extend the capabilities of 2D workspaces, they provide both the
benefits of 2D plus additional benefits from viewing the workspace at multiple levels of
detail. ZUIs offer natural parallels to the behavior of an author using a physical
workspace. When working on physical surfaces, authors frequently alternate between
focused work in one portion of the workspace and more global comparisons over the
entire workspace. An author can accomplish these same tasks in ZUIs through zooming.
To focus on the details, the author zooms in; to get more context, the author zooms out.
These multilevel views can help alleviate some of the global awareness problems that
have been reported in previous computer-based authoring tasks.
Since ZUIs allow for better overviews of the workspace, many of the
interruptions caused by expensive low level operations, such as comparing and moving
objects, are reduced. Specifically, because all objects can be brought into a single view,
the author can quickly scan and compare objects in the workspace with fast eye
movements. Spatial awareness then helps the author stay oriented in the workspace and
remember specific object positions. Information can also be quickly compared at
14
multiple levels. Individual objects can be compared, but groups of objects and groups of
groups can also be easily compared. In general, more of the constraints needed for
organizing a lot of data can be brought quickly to mind and analyzed because the
constraints are all visible at once. Likewise, many object manipulations are also made
easier with a global perspective. Perhaps the most common operation that is simplified is
moving an object from one group to a specific location within another group.
Although ZUI workspaces have several inherent benefits for authoring
presentation content, they have suffered from usability limitations that may have kept
them from being employed extensively for these types of authoring tasks. Most
importantly, ZUIs have not been well studied for use with abstract data. As a result, one
of the biggest questions in using ZUIs for authoring presentation content is how ZUIs can
provide meaningful zoomed-out views for primarily text-based content while preserving
the value of spatial arrangement.
Likewise, there is a need for improved interactions in 2D workspaces to support
an author in gradually creating presentation structure. Existing techniques for grouping
and structuring text content were also not designed for zoomable environments. As a
result, zoom-compatible techniques are needed to aid in moving from informally
organized spatial arrangements to the more formal representations used in the final
presentation media. This thesis explores a design in which information can be
incrementally and tentatively organized into multiple levels and viewed robustly at these
different levels.
Arranging information into multiple levels is a key principle in organizing it. This
enables a viewer to see the forest for the trees, that is, to see the big points without being
15
overwhelmed by the little ones. However, during authoring, the aggregation of ideas into
groups and levels is a delicate matter. A field of ideas without structure can be
overwhelming in the sheer number of objects. However, premature assignment of items
into groups and levels can also be problematic and make it difficult to move from a
mediocre organization into a better one. A design issue explored by this thesis is how a
ZUI workspace can enable tentative and exploratory creation of an organization fostering
an efficient search for a good organization of ideas for a presentation.
1.3.2 Delivering Presentations
ZUIs are also a natural medium for delivering presentations. ZUIs facilitate
putting structure on a presentation that extends beyond the slide boundaries of the
traditional slide show metaphor. Because this structure can be reflected in the
presentation's layout in the space, the structure actually becomes part of the presentation.
In moving between points in the presentation, ZUIs can reveal this structure to the
audience through viewpoint animations. This provides a natural analogue to traditional
slide show outlines or overviews. But in contrast to traditional outline slides, these
overviews remain consistent with the presentation organization because they are views of
the actual presentation structure. In addition, these overviews are inherently available at
multiple levels since the visual arrangement reflects the multi-level semantic
organization. Research also suggests that animations, such as those used in ZUIs, may
assist the viewer in integrating multiple views of structural components [7, 12].
Moreover, presenters may be more inclined to include additional overviews if they are
presented naturally in the course of transitioning between slides rather than being
explicitly inserted as additional content in the presentation.
16
By improving overviews and the visibility of the presentation structure, ZUIs may
also help the audience ask timelier and more informed questions. ZUI overviews can
naturally indicate to the audience when a topic has been completed and remind them of
topics that are yet to come. They can also indicate where more detailed information is
available on a topic but that was not covered by the presenter. This allows the audience
to confidently probe for more details, further clarification, or additional examples when
they were not included in the original presentation script.
An additional benefit to having the presentation data arranged on a single ZUI
surface is that the overviews and structure are made visible by the ZUI animations during
unexpected transitions in the presentations. The characteristics of these ZUI animations,
including length and speed, can also reflect the semantic distance between two topics.
This combination of overview and animation reinforces for the audience both the context
of where they were in the presentation and the context of where they are going. The
result is that ZUIs provide visual support to the presenter in transitioning between ideas
in the presentation. This is particularly important for improvised transitions or
movements between widely separated pieces of the presentation since presenters have not
anticipated or practiced oral transitions for these improvisations.
Presenters also typically organize their presentations conceptually using a
hierarchical structure (for example, see [33, 55]). As a result, the ZUI navigation
operations (zoom-out and zoom-in) can use this structure to facilitate a more efficient
search when jumping between ideas in the presentation. Rather than the fundamentally
linear O(n) navigation search of the slide show metaphor, ZUIs can support a less
17
complex O(log(n)) hierarchical search. This ultimately means that the ZUI navigation
controls require less attention, freeing presenters to focus on what they are going to say.
Again in addition to these ZUI benefits, usability challenges also arise in applying
ZUIs to presentation delivery. One of the primary difficulties is in navigating a zoomable
environment. Existing navigation techniques in these types of environments are
extremely powerful and flexible, however, they are often difficult to use due to the
controls necessary for navigating in an additional dimension. Improving navigation is
particularly important for presentation delivery as the presenter typically navigates the
presentation in view of the audience. In this context, mistakes can both embarrass the
presenter and waste valuable time. What is needed is a ZUI navigation framework to
support highly interactive presentations that can be traversed according to the interests of
the audience.
In addition, existing ZUI authoring tools are not specialized for presentation tasks.
As a result, many common presentation operations involving talk structure, paths and
orderings, and polished spatial layouts are not directly supported in existing ZUI
authoring tools. Yet, before a presentation can be delivered using a ZUI, each of these
individual components must be specified. Because these components multiply the
amount of work needed to author a ZUI presentation, one of the biggest obstacles to
using ZUIs for presentation delivery is providing tools to simplify or, better still,
automate these tasks. Yet, these simplified tools must also provide the presenter with
ultimate control in mapping semantic attributes, such as order and hierarchy, to visual
attributes, such as positions and sizes.
18
Lastly, the ZUI environment also raises the conceptual question of whether the
slide metaphor is still appropriate. The ability to view the presentation structure in a
single continuous space at multiple zoom levels introduces issues around how to best
represent information relationships, both for utilizing human spatial abilities, readability,
and general visual appearance.
1.4 Niagara
The usability challenges for authoring presentation content are addressed in this
work in a tool called Niagara, shown in Figure 1. Niagara provides a freeform zoomable
workspace with objects and interactions to support authoring presentation content while
avoiding the most common authoring distractions. Several new interaction techniques
are introduced to deal with the complexities of authoring in ZUIs.
One of the most important techniques introduced here for text authoring in
zoomable environments is automatic text reduction. Automatic text reduction combines
standard geometric zooming and summarization to create more meaningful views of text
objects when zoomed out. These enhanced zoomed-out views allow the author to
maintain an awareness of object content while also mimicking the global context
provided by zoomed-out overviews.
Another important technique for authoring presentation content is automatic
grouping. Automatic grouping acts as a bridge between the formal structures used in
more polished organizations, such as slides and folders, and the informal organization
that is used in early brainstorming and clustering of ideas. Automatic grouping allows
the user to explicitly create, modify, or destroy groups of objects simply by changing
object arrangements in the space. These groups can then offer many of the advantages of
19
more formal representations including: having a label, supporting group-specific system
operations, and having the ability to be modified by the user as a group. Moreover, these
groups can be assigned unique colors to further improve the recognizability of groups of
text objects when zoomed out.
Figure 1: A screenshot of Niagara being used to author parts of this document.
1.5 CounterPoint
This work also introduces CounterPoint, shown in Figure 2, as a presentation tool
based on the ZUI metaphor. As such it offers solutions to the problems presenters face
with the slide show metaphor. CounterPoint also presents solutions to many of the
common usability challenges associated with ZUI environments. CounterPoint simplifies
the authoring of zooming presentations by extending the slide metaphor to zoomable
space. As a result, much of the complexity of authoring in a ZUI environment is
simplified to the more familiar task of slide layout using commonly available tools.
20
CounterPoint then provides tools to organize the individual slides into a single
unified hierarchy. CounterPoint uses this hierarchy to simplify several of the ZUI
specific authoring tasks. First the spatial layout of the slides can be created based on this
structural organization. Similarly, CounterPoint can also use the structural hierarchy to
determine a default ordering of the slides for the presentation's scripted path.
Figure 2: A screenshot of a CounterPoint presentation on PhotoMesa [6].
1.6 Contents
Chapter 2 describes previous work relating to ZUI authoring presentation content
and the research involved in designing Niagara. Chapter 3 describes previous work
related to computer-based presentation delivery and the research involved in designing
CounterPoint. Chapter 4 discusses Niagara?s unique features and some of the more
21
interesting implementation details. Chapter 5 describes CounterPoint?s architecture and
some unique features. Chapter 6 offers some areas for future work in both presentation
authoring and delivery. Chapter 7 concludes with a summary of the major results and
contributes of this dissertation. Appendix A provides the questionnaires used in various
user studies throughout the dissertation.
22
Chapter 2:
Authoring Presentations and Niagara
2.1 Related Work
Computer supported authoring tools have been one of the long term goals of
computer science research, dating back to the work of Bush [15], Sutherland [108], and
Engelbart [31]. As a result, there have been many computer systems focusing on many
different aspects of computer supported authoring. One particular subset of computer
authoring tools has focused on using 2D workspaces for authoring and organizing
abstract information. Some of the more related systems to the current work include
Aquanet [68], Beach [90], Cognoter [33], Data Mountain [93], Dolphin [45], Flatland [74],
gIBIS [20], the Interactive Mural [44], MUSE [37], Notecards [46], OneNote [79],
PowerPoint [89], Storyspace [107], TinderBox [110], Tivoli [84], VIKI [70], VKB [100],
and Web Squirrel [115].
Niagara builds on individual aspects of many of these tools as described below.
In addition, although previous systems address similar tasks to those addressed in this
chapter, they have not been systematically studied to determine which user interface
techniques are best suited for different authoring tasks. Nevertheless, some of the most
related evaluations below are reviewed below.
2.1.1 Authoring Systems and Techniques
The work in this chapter concentrates on reducing the formality of 2D workspaces
for authoring abstract data to better match the tools to the task. A similar emphasis can
also be found in the work of Marshall and Shipman, who have explored this idea of
reducing formalityin a progression ofauthoring tools starting with Aquanet [68], then
23
VIKI [70], and most recently VKB [100]. Each of these systems was built around a free-
form 2D workspace where the author could create, organize, and structure information in
different ways. In the Aquanet system, users were able to specify various characteristics
of their information through schemas or templates. Marshall et al. found that users
tended to avoid these formal specifications whenever possible [68]. As a result, the
subsequent tools, VIKI and VKB, allow the user to signify relationships through spatial
arrangement and visual characteristics. These types of informal structures allow users to
refine and evolve relationships over time rather than having to specify them up front.
Marshall, Shipman, and colleagues have concentrated on the different kinds of structure
and representations that allow for emergent structure. However, unlike the work in this
chapter, they have not concentrated on studying the various user interface elements and
interaction techniques employed for the task.
There are also commercial implementations of authoring systems that use spatial
layout and visual attributes for structuring text. Several systems are sold by Eastgate
Systems including Web Squirrel [115] and the more recent TinderBox [110]. Web
Squirrel is a 2D workspace for organizing internet shortcuts. Users can organize their
shortcuts spatially or by dividing them into explicit collections. TinderBox has many of
the same features and interactions as Web Squirrel. However, rather than focusing on
web shortcuts, TinderBox supports creating and organizing arbitrary text notes. Microsoft
is also preparing to release a related system called OneNote [79]. OneNote virtually
emulates a physical notebook. It allows authors to create and spatially organize notes,
lists, images, URLs and other objects on virtual 2D pages.
24
The software prototypes described in this chapter differ from these previous
systems in that they introduce several new techniques for interacting with text objects in a
zoomable 2D workspace. Two of the most prominent interface techniques introduced in
our work are automatic text reduction, a semantic zooming technique for text, and
automatic grouping, an interaction technique to facilitate a natural transition from spatial
clusters to formal structure. Previous work relating to these two features is described
below.
2.1.1.1 Semantic Zooming of Text
Niagara uses the ZUI paradigm for authoring and organizing text. As a result,
strategies for presenting text in zoomable environments are an important component of
this work. One well known technique used to represent objects in zoomable
environments is semantic zooming. Semantic zooming involves changing an object?s
appearance based on the current zoom level and has been an integral part of Zoomable
User Interfaces since they were first created [9, 10, 85]. Perlin and Fox? original ZUI
paper [85] even describes a semantically zoomable text object: ?When text is visibly
small it appears only as a title. As the user zooms in, this expands to include an abstract.
Further zooming reveals first an outline with short text descriptions, then finally the full
text.? A host of previous systems have employed this kind of semantic text zooming.
One specific example is the DOI tree [17] which switches between several predefined text
representations based on available space. Our approach differs from this one in that it
does not require pre-defined levels of abstraction. Instead, it dynamically generates text
representations based on the space requirements at the current zoom level using various
linguistic techniques.
25
Text representations were also addressed in the work of Shipman et al. They
proposed reducing text object sizes to address limited display space in a 2D authoring
environment [102]. Their work concentrated on allocating space for a collection of text
objects based on multiple foci visualization techniques. In contrast, our text reduction
technique focuses on automatically generating textual representations for changing space
requirements. Restated, the technique of Shipman et al. allocates space for objects while
our technique creates more meaningful representations for text objects.
Another technique used to improve reduced size representations of textual
documents is enhanced thumbnails [117]. Enhanced thumbnails overlay highlighted text
labels on top of ordinary web page thumbnails. These thumbnails were shown to offer
both the benefits of ordinary thumbnails plus the benefits of text summaries for finding
information in web searches. The enhanced thumbnails technique makes use of structure
in the HTML document and keywords used in the internet search. In contrast, the
automatic text reduction technique described in this chapter does not rely on these
assumptions. Instead, automatic text reduction is intended to provide more meaningful
reduced size representations for arbitrary text objects.
Reduced text representations have also been the focus of a large body of work
relating to summarization [67]. In summarization, the goal of the text reduction is to
identify the most salient information. A summarized text is intended as a stand-in for the
longer text and should be comprehensible, correct, salient, and representative. In Niagara,
the goal is different. Text need not carry the full meaning and need not be
comprehensible or strictly correct. Instead, Niagara?s reduced text representations are
intended to act as recognizable visual cues and reminders of the full text. Niagara?s
26
reduction algorithms are also combined with zooming to dynamically access a range of
reduced representations for a given object.
2.1.1.2 Automatic Structuring Behaviors
Moving from informal spatial organizations to more formal structures, such as
outlines, is also an important component of authoring in Niagara. One previous
technique used to create structure from spatial organizations is spatial parsing. VIKI
implemented a form of spatial parsing based on object positions, sizes, and other visual
attributes that was used to infer object relationships [103]. These inferred relationships
are then shown to the author in selecting groups of objects, suggesting formal collections
of objects, and suggesting composite types or templates. Niagara?s automatic grouping
differs in that it is invoked explicitly by the user when objects are moved close together.
This immediately and persistently reflects the inferred groups to the author. Niagara also
automatically modifies object colors when adding or removing objects from a group.
Although the techniques in both systems are intended to reduce the overhead of creating
structure, they otherwise differ in purpose. VIKI allows users to create customized
composite types and relationships using position and visual properties; Niagara uses
automatic grouping and color to create representations that emphasize group membership
and enhance recognizability under zooming.
Another system, WebSquirrel [115], uses a variant of spatial positioning to
determine group membership. In WebSquirrel, a user can establish a labeled signpost for
a group. Items near a signpost or near other items near a signpost are then treated as
belonging to the group and a bounding rectangle is drawn surrounding the group items.
One of the problems with this technique is that group membership can potentially be
27
confusing or unpredictable. For example, when groups are moved near each other,
WebSquirrel can re-assign items from one group to another according to relative
distances to signposts. A similar difficulty is that if an intermediate item in an ?island
chain of items? is moved out of a group, then the distant part of the chain becomes
isolated and is no longer considered part of the group. Both of these problems are solved
in Niagara by making group assignment be an explicit user-directed operation. An object
is only added or removed from a group when the user explicitly moves the object into or
out of the group.
Another recent system from Microsoft, called OneNote [79], also uses explicit
user actions to automatically create structure. OneNote automatically adds, removes, and
indents objects in bulleted lists when they are moved in the OneNote workspace. Objects
can be positioned anywhere in the OneNote workspace, but they can only be structured
into well-aligned, hierarchical lists. In contrast, Niagara does not enforce any layout
constraints on automatically created groups. This allows authors to position objects
within groups to informally indicate relationships.
Components of the Beach [90] groupware system also provide techniques for
moving from an unstructured 2D workspace to a hierarchical organization. The system
provides interaction rules for different kinds of objects that help in gradually creating
structure. In particular, title objects exert a kind of ?magnetic? force that can be used to
attract and formalize a cluster of related objects in the workspace. Niagara?s approach
differs from this approach in that group objects never have to be explicitly created.
Instead, groups are created and deleted simply by changing the position of objects in the
workspace.
28
Manyother systems such as PhotoMesa [6] use automatically assigned colors as
part of a scheme to represent group membership. Again, Niagara differs from these in
that it supports a user-created, free-form layout rather than enforcing a structured layout
such as a treemap. Moreover, Niagara also creates its groups and assigns colors based on
the user?s explicit actions in positioning objects in the 2D workspace.
2.1.2 Evaluations
This chapter describes a small qualitative user study consisting of 4 subjects that
looks at how people?s behavior differs in organizing information for a presentation using
several different kinds of 2D workspaces including PowerPoint, sticky notes on a
whiteboard, and our own software prototype. A number ofprevious user studies have
looked at similar tasks using similar 2D authoring environment. These previous
evaluations are described in the first section below.
This chapter also presents two larger quantitative studies comparing ZUIs to more
traditional techniques for organization tasks. Our first quantitative study compares the
performance of 13 subjects organizing shapes into groups based on common visual
properties. The two interface conditions compared in the study are a ZUI and a folder-
based interface. Our second quantitative study replicates the previous study but replaces
shapes with text objects. This second study consists of 14 subjects.
No controlled studies that we are aware of have compared ZUIs to other interface
techniques for this type of task. Nonetheless, there have been a number of studies that
have compared ZUIs or zooming to different interface techniques for other types of tasks.
These studies are described in the second section below.
29
2.1.2.1 Evaluations of 2D Authoring Environments
Jones and Dumais performed several experiments exploring the benefits of 2D
spatial location versus naming for a news article retrieval task [60]. This study did not
directly involve computers but rather compared spatial location with naming as filing
strategies for paper notes. The tasks in the study required subjects to read articles and
then file paper notes with numbered references to the articles using one of 6 conditions:
1) a subject-selected position of a numbered label on a piece of paper, 2) a randomly
ordered paper list with subject-selected names, 3) a subject-selected position of a named
label on a piece of paper, 4) a combination of the named list and the numbered label
position, 5) the numbered label position on a paper schematic of an office, and 6) the
numbered label position in a physical office. The study consisted of a total of 120
subjects and used a between subjects design. The results from the study revealed that
retrieval with location-only cues was significantly less accurate than with name-only
cues, even when names were limited to 2 letters. Retrieval of numbered labels in the
physical office was also less accurate than with the name-only cues on paper, though the
difference was only marginally significant. One potential problem in this study is that
they assumed that the value of spatial location comes from choosing an object?s position.
However, in their own literature review they cite several studies that demonstrate an
incidental and effortless memory for the location of information, such as the position of
text on a page in a book. This suggests that merely seeing an object?s position, rather
than choosing it, may trigger spatial memory. The study is also not representative of
actual work practice since the spatial arrangements used in most real-world applications,
such as those described in this chapter, are reinforced by rearranging existing objects as
new objects arrive. The studies described in our work essentially explore these same
30
issues of naming versus location but focus on an organization task in a more realistic
setting.
Notecards was one of the earliest computer-based 2D authoring environments to
be studied [46]. Notecards was a hypertext authoring environment that allowed the author
to create virtual note cards that the user could then organize and link together. The
system also allowed many notes to be open at once in separate windows. The author
could then organize these windows spatially in the Notecards workspace. Monty
performed a longitudinal study of a single author using this system over a 7 month period
for a paper writing task [72]. Monty identified a number of common operations in the
author?s writing process as well as problem areas in the tool?s support of these processes.
The qualitative study in this chapter differs from this study in that it focuses on the
similarities and differences in the organization component of an authoring task across 3
different tools. The study also includes multiple subjects to identify common operations
across different users.
A similar longitudinal usage study was performed for the Aquanet system [68]
(system described above). This study consisted of a small set of Aquanet users making
senseofalargecollectionofinformationoverthecourseof2years. Thestudyfocused
on the kinds of structure and relationships people created in authoring with Aquanet. The
study described in this chapter differs in that it identifies behaviors across several
different authoring tools. The current study also specifically focuses on organization
behaviors and does not explore the issues of linking or argument structure.
Marshall et al. also performed a qualitative study of the VIKI system [69] (system
described above). This study looked at 15 subjects performing a time-constrained task
31
that required the participant to make a product purchase decision based on information
they gleaned from 75 articles. Subjects performed the task using either paper copies of
the articles and standard office supplies or electronic copies of the articles and a version
of VIKI. Although it compares similar tools to those compared in the study in this
chapter, the VIKI studyaddresses a different task than this work. As mentioned, the
study in this chapter focuses on the organization component of a presentation authoring
task.
A more recent study by Prante et al. [90] looked at 3 commercially available
groupware tools for brainstorming and organizing ideas among co-located groups. The
study looked at 45 subjects performing a set of 3 creative tasks using a different one of
the 3 groupware tools for each task. Subjects solved the tasks in groups of 3 connected
via networked computers. Despite being in the same room, subjects were not allowed to
talk to one another. The first tool used in the task provided a structured mind-mapping
interface and required subjects to take turns in modifying the workspace. The second
tool provided an unstructured whiteboard-like surface that also required subjects to take
turns in modifying the workspace. The third tool provided a similar whiteboard-like
surface but allowed multiple participants in the group to modify the workspace at the
same time. The study used a within subjects design and found that subjects using the
simultaneously editable workspace created significantly more objects than in the other
turn-taking tools. More relevant for this work, subjects using the structured workspace
created more ideas than those using the unstructured workspace. In general, this study
looks at similar kinds of tasks to those in this chapter. However, the work in this chapter
is primarily concerned with an individual?s process in performing the tasks rather than a
32
group. Moreover, this chapter focuses on the organizing component of an authoring task
rather than idea generation.
2.1.2.2 Evaluations of ZUIs and Zooming
One of the earliest studies involving zooming was performed by Beard et al. [5].
This study compared scrollbars to an overview window for navigating a balanced binary
tree. The study also included two different kinds of controls for the overview window.
The first control type, described as ?roaming,? involved dragging a detail region in the
overview to change the view in the main window. The second control type, described as
?roam and zoom,? required the user to drag a new rectangle in the overview each time to
specify the view in the main window. This allowed the user to zoom out and zoom in by
dragging bigger and smaller rectangles respectively. The study consisted of 6 subjects
and had a within subjects design. The primary result from the study suggests that using an
overview window is faster than scrolling for navigation in certain kinds of environments.
No significant difference was observed between the different overview controls. The
studies in the current chapter differ from this work in that they involve organizing
information rather than simply navigating it. In addition, the kind of zooming studied in
this chapter is also different in that it is driven by directly interacting with the workspace
rather than with an overview window. The zooming described here is also animated to
assist the user in understanding how the view has changed.
Ghosh and Shneiderman performed a related study comparing an overview plus
detail interface with a zooming-only interface for visualizing timeline data [41]. The
tasks in this study required subjects to find answers to information contained on the
timeline. The study included 14 subjects and had a between-subjects design. The study
33
presents a borderline significant result suggesting that overview plus detail is slightly
faster than the zooming only condition. The studies in this chapter again differ from this
study in that they look at different tasks. In addition, the data spaces in this study are
essentially one dimensional rather than 2D as typically occur in ZUIs.
Another comparison ofzooming and overview windows was performed by
Hornb?k et al. [53]. This study compared a ZUI plus an overview window to a ZUI
without an overview window for a set ofmap tasks. The ZUI in both conditions allowed
the user to pan, zoomin, and zoomout using mouse gestures. The overview window
allowed the user to move and resize a detail rectangle to change the view in the main
window. The study included two kinds of tasks including navigating to a particular target
and browsing for specific map features. The study also examined a single level map,
where all objects were labeled with the same size font, and a multi level map, where map
labels were sized according to the geographic object that they labeled. The study
consisted of 32 subjects and used a within subjects design. The primary results from the
study indicate that subjects are faster using the ZUI without an overview when the task
involved the multilevel map or navigation. Here again, this study differs from the
controlled studies in this chapter in the type of task examined. Rather than map-based
tasks, this chapter examines tasks involving abstract information. Likewise, rather than
browsing or navigation tasks, the current chapter looks at organization tasks.
Plumlee et al. performed a similar comparison of zooming versus a multiple
window strategy [88]. The tasks they investigated involved comparing groups of shapes
laid out in a large 2D workspace to determine whether a probe group matches a sample
group. Tasks varied in the number of shapes to be compared between groups. Prior to
34
running a user study, the authors present a cognitive model of the task based on the
capacity of working memory. They then used this model to predict the performance of a
zooming interface versus a multi-window interface for the task. The model predicted that
the multi-window interface would perform better as the number of objects in the groups
increased. They then performed a user study with 17 users comparing these two interface
techniques for the group comparison tasks. The study analysis used a within subject
design and found a significant effect of interface type on completion times. This study is
quite similar to the studies performed in this chapter. However, this study has
intentionally negated some of the benefits of zooming by using textured backgrounds on
the zooming workspace to camouflage objects. In contrast, our work tries to preserve as
much ofan object?s value as possible when zoomed out. In addition, rather than
comparing ZUIs to multi window strategies as was done in this study, the studies in this
chapter compare ZUIs to a folder or paging strategy.
Combs et al. compared ZUIs to several other commercially available image
management systems for a collection of image retrieval tasks [19]. The ZUI used in this
studyprovided a zoomable grid ofimages in which the user could zoomand pan. The
other systems compared in the study used a 2D grid of thumbnails, a layout of images on
a 3D plain, and an arrangement of images in a spin-able 3D ?lazy susan.? The study used
two image tasks including one that involved searching for a specific image and the
second that involved browsing for an image to send to friend. Three different sized
image sets were also examined including 25, 75, and 225 images. Thirty subjects
participated in the study, which combined a within subjects and a between subjects
design. The results from the study indicated that the ZUI and the 2D image browsers
35
have lower task completion times and higher subjective satisfaction than the 3D
browsers. As with the other previous studies of ZUIs, these differ from the controlled
studies in this chapter in that they only look at searching and browsing and do not look at
organization. Moreover, it is expected that graphics-heavy tasks are more naturally
suited for ZUIs than the textual tasks in this chapter since graphics retain more of their
value when reduced in size.
Another study, by Paez et al, looked at using ZUIs for a document reading task
[80]. They performed a between-subjects experiment with 36 participants comparing a
document laid out in a ZUI workspace to a hyperlinked document in a traditional web
browser. The task in the study required the user to find the answer to 5 questions within
the document using one of the two interface types. They found no significant differences
between interface types in completion times, comprehension measures, disorientation
measures, or subjective satisfaction. However, subjects in the study did report that the
ZUI provided good skimming and overview capabilities and was easy to learn. This
study is interesting since it shows that users were able to navigate a ZUI workspace to
complete a reading-heavy task. The studies in this chapter extend this work by looking at
tasks that involve authoring in addition to reading text.
Looking again at hypertext, Hightower et al. compared a web browser with a ZUI
history tool to an unaided web browser in two usability studies [52]. The ZUI historytool
laid out thumbnails of visited web pages in a 2D tree representing their hierarchical web
history. The user was then able to interact with the toolto navigate the tree and reload
previously visited web pages into the browser. The tasks used in the first usability
studied required subjects to find particular facts on individual web pages and make
36
comparisons between groups of web pages. This first study consisted of 36 subjects and
used a within subjects design. The results from the first study indicated a subjective
preference for PadPrints and a decrease in the number of pages accessed with PadPrints.
The second study required subjects to navigate a group of web pages to find the answers
to an initial set of questions. They were then asked a set of questions that required them
to return to pages visited in the process ofanswering these questions. The second study
consisted of 36 subjects and also used a within subjects design. The results from this
second study indicated faster completion times with PadPrints for the tasks that involved
revisiting pages. Page accesses were also again lower with the PadPrints condition.
Once again the studies in this chapter differ from the PadPrints study in that they address
authoring rather than navigation or search tasks.
Robertson et al. carried out a related study comparing a spatial interface called
Data Mountain to the more traditional hierarchical system in Internet Explorer 4 for
organizing web favorites [93]. The Data Mountain presented the user with an inclined
plane on which they could spatially arrange thumbnails of bookmarked web pages. The
Data Mountain interface was not a traditional 2D workspace and did not support a
traditional notion of zooming. However, as web page thumbnails were moved closer to
the bottom of the screen they grew in size, effectively giving the user a limited zoom
control on individual pages. In addition, the interface provided a mechanism to view a
single web page in detail which essentially ?zoomed in? even further on the individual
page. Robertson et al. then performed a user study that required subjects to create and
organize bookmarks for 100 web pages. Once the organization was completed, subjects
were then given tasks asking them to find a particular bookmark based on 4 different
37
cues. The cues included title, summary, thumbnail, and combination of all three. The
study consisted of 32 users and used a between subjects design. The study results
indicated that the spatial interface reduced the time to store the bookmarks as well as
reducing the number of retrieval errors made. The bookmark organization task in this
study is similar to the organization task explored in this chapter. However, rather than
focusing on retrieval, our work focuses on the benefits of spatial arrangements for the
organization task. In addition, our work examines organization of text only content
rather than more visual web page thumbnails.
2.2 Niagara
The authoring model described in the introduction included four phases:
generating ideas, organizing, composing, and revising. Although there is certainly
research needed in each of these areas, this chapter focuses on the organization
component of the process. One of the reasons for this focus comes from previous
research in computer based authoring environments, such as Notecards [46]andVIKI
[101], that suggests that many of the difficulties in authoring tasks arise in defining the
work?s structure and the relationships between objects. Further, the organization
component seems to be one of the places where the problem of getting an overview of the
text is particularly difficult on a computer. In the Notecards experiment described above,
the author using Notecards in the study reported a desire to print his notes and ?spread
them out on a table? in order to organize them [72]. Similarly, the research cited by
Severinson Eklundh [97] suggests that organization is one of the places where computer-
based authoring tools provide insufficient overviews.
38
This chapter addresses these text organization challenges in the specific context of
authoring content for presentations. However, as discussed in the introduction, there are
similar constraints to those in presentation authoring across many kinds of text-authoring
tasks. In addition, the interaction techniques described in this chapter make no explicit
assumption as to the final use of the organized information. Consequently, the
techniques described in this chapter are likely to apply to many different authoring tasks.
The techniques described in this chapter for organizing text are implemented as
part of an authoring tool called Niagara. A primary goal in developing Niagara was to
understand the extent to which improved interactions and automation on a small but
active display could reclaim the overview capabilities inherent in large passive physical
surfaces such as whiteboards and tabletops.
Niagara was refined through a cycle of building prototypes and evaluating the
prototypes with user studies. Below, three iterations in this cycle are described. In each
iteration, insights were discovered into presentation authoring and the optimal computer
interfaces needed to support this task. These three iterations led to a controlled study
comparing folders to zooming for a shape-based organization task. This shape study was
then duplicated in a nearly identical study involving text. Finally, the chapter describes
some initial results involving real users organizing information for real presentations.
2.3 First Design Iteration - Niagara version 1
Because it addresses problems similar to those addressed by spatial hypertext
systems, our work began with a prototype similar to several well known spatial hypertext
systems including VIKI, VKB, and Web Squirrel [70, 100, 115]. This prototype provided
a starting point that allowed us to observe actual users interacting with the system to see
39
what worked and what didn't. Figure 3 illustrates a populated workspace in Niagara
version 1.
Figure 3: A screenshot of the first version of Niagara with text objects and collections.
The goal of Niagara?s freeform workspace is to allow the author to easily create
and maintain information. The meaning of the information in the workspace is held both
in the content (that is, words) as wellas in the spatialarrangement. An author tends to
locate closely related items near each other so that they can be conveniently used
together. Parallel arrangements of items suggest parallel meanings. Persistence of
arrangement speeds access in that the author roughly remembers the location of items and
does not need to search for them. It also speeds understanding when an author uses
parallel visual structures to imply parallel meanings. The user invests time to arrange
40
items, and presumably benefits both from convenient use and speedier access because he
remembers where things are.
There is a delicate balance in supporting an author using a 2D workspace like
Niagara?s. If the system automatically changes an author?s arrangements, the author may
have to invest time and energy to learn the new arrangement and possibly to rearrange it.
Conversely, if the system provides no assistance in positioning objects, then the user can
waste time aligning objects or making space for new objects. Reflection on our own
computer authoring experiences suggested that a significant amount of time was devoted
to this latter case of rearranging and aligning objects. Our reflections were reinforced by
research suggesting that spatial arrangements that involve overlap are undesirable and
could even lead to users losing objects [93]. Consequently, Niagara implemented a no-
occlusion contract in its initial version. This contract prevented objects from obscuring
one another so that objects could not become hidden. Whenever objects were moved,
were resized, or grew to accept new content, they automatically bumped other objects out
of the way. This contract brought a physical quality to the interface so that every object
could be used as a tool to align or bump other objects out of the way. More specific
object interaction contracts were also implemented to automate the creation of common
alignments. Further details of the specific features and interactions in this version of
Niagara are described in Chapter 4 on Feature and Implementation Details.
2.3.1 Study
Following the development of this first Niagara prototype, a small informal
observational study using two subjects was performed. The subjects were told to
organize a set of short text passages into groups with the ultimate purpose of presenting
41
the information. The task consisted of several supporting operations such as reading the
passages, clustering related items, creating and labeling collections, and repairing the
evolving structure. The text passages for the study were taken from the California
Drivers Handbook. Supplying the content in this way provided some control over the
scope of the organizations created and also simplified the task by eliminating some
activities such as generating or gathering information.
This study quickly illuminated a recurring problem with the Niagara version 1
prototype. As expected, the number of text items quickly exceeded the available screen
space. Once this occurred, the users had problems managing the populated workspace as
it extended beyond their immediate view. As a result, a significant amount of panning
was necessary to manage the widely distributed objects. Operations such as finding and
moving items became correspondingly more expensive and dominated the task
completion times.
2.4 Second Design Iteration - Niagara version 2
The most significant change to the Niagara interface that resulted from the initial
informal study was the addition of a spatial overview, shown in the upper right hand
corner of Figure 4. The overview was meant to address the problems that subjects
encountered in managing a workspace that were larger than the containing window. As
with overviews in other types of programs [105], it was expected that the spatial overview
in Niagara would provide a mechanism for rapid navigations as well as a general aid to
spatial awareness. In addition, the overview was also expected to accelerate one of the
most common operations, namely moving an item to a known collection. Rather than
needing to navigate to an off-screen collection, users could drop objects into off-screen
42
collections directly in the overview. This behavior was further encouraged through
semantic zooming on the titles of collections (see Figure 4) to maintain their readability
in the overview.
Figure 4: A screenshot of populated workspace in the second version of Niagara. A
spatial overview is available in the top right corner of the workspace.
2.4.1 Study
A second observational study was conducted with the second Niagara version
containing the spatial overview. This study was intended to provide data on the
frequencies and costs of operations involved in this grouping task. This would allow for
the identification of any bottlenecks caused by the interface. In order to highlight the
tradeoffs of different interface techniques, the study also asked subjects use PowerPoint
43
and sticky notes on a whiteboard. PowerPoint was chosen based on informal feedback
from colleagues indicating its use in similar organizational tasks. Similarly, sticky notes
on a whiteboard were included because they represent the optimal organization
environment, offering tangible interactions and abundant display space.
While it was expected that display size played a dominant role in a user's
performance on the task, the study was intended to identify the interface interactions that
were most limiting to a user performing the task. Rather than trying to carefully isolate
the influence of a specific set of variables, this study was meant to explore the different
overall affordances provided by Niagara, PowerPoint, and sticky notes on a whiteboard.
2.4.1.1 Method
The subjects were 4 employees from PARC. Three subjects were members of
research staff and one was an administrator. The content used in the study was 3 sets of
60 facts, containing 6 to 60 words, again taken from the California driver's handbook. In
the sticky notes condition, the facts were printed on sticky notes such that each sticky
note contained a single fact. In the software conditions, the facts appeared one at a time
on an "electronic tablet" where the user could drag them into the application. Figure 5
shows a pilot subject using sticky notes to complete the organization task.
The experimenter provided a short tutorial and training period in Niagara and,
when needed, PowerPoint. Each trial was limited to 30 minutes or was completed when
the subject was satisfied with the grouping of all 60 items. Subjects performed the
grouping task using all three tools in a single session. Tool and fact set orderings were
counterbalanced to minimize any sequencing effects.
44
Figure 5: A pilot subject organizing the driving facts using sticky notes on a whiteboard.
All sessions were videotaped and subjects were asked to use a think-aloud
protocol. A set of descriptive activities was identified to be used as a set of tool
independent operations to code the video. These operations are described in Table 1.
Two important components of the task that are noticeably absent from this chart include
reading and thinking. These were not counted since they frequently occurred without any
visible or audible manifestation. Lastly, since this was primarily an exploratory study,
inter-rater reliability was not computed for the coded video.
Following the study, subjects completed a short questionnaire regarding their
experiences using each of the tools. This questionnaire is available in Appendix A.
45
Table 1: A description of the operations coded in the video.
Operation Description
Create Collection Creating an explicitly defined collection ? with borders or an
initial title
Labeling Changing a label or adding a label to a cluster or a category
that was left unlabeled when created
Navigation Panning, Scrolling, or Zooming (only applicable for software
conditions)
Non-Task Adjustment Non-navigational operations that did not have semantic
meaning for the task
Placement Moving an item from the ?tablet? to the workspace
Reclassify Moving an item from one explicitly defined category to
another
Spatial Clustering Changing the position of an item to indicate a semantic
relationship
2.4.1.2 Results
Single factor ANOVAs with repeated measures were used to analyze the results in
preference to t-tests since the study compared 3 study conditions. Post hoc tests were
then used to reduce the chances of Type I error in performing multiple comparisons. In
particular, Scheff? tests were used as they are conservative yet more sensitive for
complex (in addition to pairwise) comparisons between conditions than Tukey tests.
If should be noted that parametric tests, including ANOVAs and t-tests, are used
throughout this dissertation since the research involves human subjects measures, which
tend to be normally distributed [53]. Even when the normality assumption is violated, the
t-test and ANOVA are still likely to be valid [53].
Single factor repeated measure ANOVAs were used to evaluate the effect of tool
type on completion time, on the frequency of operations coded in the video, and on
subjective satisfaction. The means for the collected data are shown in Table 2. These
analyses found a significant effect of tool type on completion time (F(2,6)=11.12, p =
46
0.01), non-task adjustments (F(2,6)=13.68, p = 0.01), collection creation (F(2,6)=40.32, p
< 0.001), collection labeling (F(2,6)=6.37, p = 0.03), and for the questionnaire item
?What is your overall reaction to using Post-It notes for this task?? (1=Frustrating,
5=Satisfying) (F(2,6)=10.34, p = 0.05).
Post hoc Scheff? tests indicated that sticky notes were significantly different from
both the software tools in: shorter completion times (p = .01), fewer non-task related
adjustments (p = 0.01), fewer collection creations (p < 0.001), more collection labelings
(p = 0.04), and higher subjective satisfaction (p = 0.05). Further Scheff? tests indicated
that significantly fewer non-task related adjustments were made (p = 0.03) and more
collections were created (p = 0.01) in PowerPoint than in Niagara.
Table 2: The means and standard deviations of dependent variables measured in the
study.
Stickies PowerPoint Niagara
Completion Time (min.) 21.14
(s 1.65)
28.07
(s 3.87)
30
(s 0)
Create Collection 1.0
(s 2.0)
9.0
(s 1.41)
6.25
(s 3.40)
Labeling 9.0
(s 6.16)
0.25
(s 0.5)
3.25
(s 3.95)
Navigation 0
(s 0)
17.75
(s 7.8)
14.0
(s 17.26)
Non-task Adjustments 0.75
(s 1.5)
18.75
(s 14.77)
36.5
(s 9.18)
Placement 60.0
(s 0)
49.75
(s 16.17)
57.25
(s 4.27)
Reclassify 3.25
(s 4.03)
21.0
(s 12.14)
19.75
(s 11.93)
Spatial Clustering 16.25
(s 6.02)
1.5
(s 1.73)
5.5
(s 4.51)
Subjective Satisfaction
(1=Frustrating, 5=Satisfying)
5.00
(s 0.0)
2.75
(s 1.26)
2.75
(s 1.26)
47
2.4.1.3 Discussion
One activity that differentiated all 3 tools was non-task related adjustment. These
types ofadjustments were likelyminimized in the whiteboard condition due to the highly
intuitive and well practiced physical interactions. In contrast, these adjustments seemed
to be particularly common in the Niagara condition because of quirks, bugs, and
performance limitations that were a result of the software's experimental nature.
The difference between the number of collections created in Niagara and
PowerPoint is likely a result of the different spatial paradigms suggested by the tools.
PowerPoint presents space as slides, where each slide is a disjoint white rectangle
surrounded by an infinite gray plane. This seemed to encourage people to use the visual
slide boundaries as semantic divisions as well. In contrast, Niagara presents space as a
set of nested infinite surfaces. These infinite surfaces seemed to make less urgent the
need to divide objects into explicit collections since users were able to indicate groups
through whitespace instead.
The remaining differences in completion times, collection creations, and labelings
between the whiteboard and the software tools are likelyto be the result ofthe
whiteboard's larger display size. Because it offered virtually unlimited display space for
this task, the whiteboard allowed users to spread out their information yet still be able to
get an overview of that information. Similarly, having the larger surface allowed subjects
to quickly scan all the facts with head or eye movements whereas collections on the
software tools often required more time-consuming mouse or keyboard interactions for
an equivalent scan.
48
Because the software tools have less display space, users had to develop strategies
to compensate for this limitation. One of the first strategies observed was that subjects
frequently placed new items into an unlabeled region in the workspace to help identify
patterns. This technique was named "staging" because items passed through an initial
scanning and comparison stage prior to a more formal organized stage. Once a pattern
was detected in this staging area, a new collection was created and the matching facts
were moved to that collection. Collections allowed subjects to reclaim display space by
reducing the amount of space used by similar facts. In PowerPoint, each collection was
typically represented by a single slide that could be reduced to an icon and title in a
column on the left edge of the screen. In Niagara, each collection was a rectangular
window inside a larger workspace that reduced the scale of the contained items and
clipped these items to the boundary of the window. Once similar items were put into
collections in either tool, subjects were left with more room to process newly arriving
facts.
An early assumption was that labels would be necessary for quickly deciding
which group an object belongs to. It was predicted that without labels, subjects would
frequently need to reread the items in a cluster to recall its membership criteria. Clearly
the small number of labeling operations in the sticky notes condition does not support this
prediction. Instead, this behavior is likely to depend on the number of items organized.
Since the maximum number of collections subjects created in the study was 10, subjects
were likely able to remember a large fraction of these membership criteria at any one
time. In the cases where the subject could not remember, a quick scan of one or two
items usually served as a sufficient reminder.
49
Despite their limited use in the whiteboard study condition, labels are likely to be
more important in actual presentation authoring tasks that are completed over a number
of days rather than in a single half hour session. Over extended periods, the author is
more likely to forget group contents and positions between the authoring sessions. In
these cases, formal labeled collections are likely to compete with informal spatial
clustering as a grouping strategy (see Table 3). In choosing between spatial clustering
and labeled collections the user makes tradeoffs between work up front and work later.
Table 3: Tradeoffs the organizer makes in using spatial clusters vs. formal collections.
Time To:
Create Modify Determine Membership
Criteria
Informal Spatial
Clusters
Low Low Medium/High
Formal
Collections
Medium/High High Low
One last practical observation from this study was that the spatial overview in our
first prototype was not effectively utilized. One problem with the overview was that it
occluded a portion of the workspace so that objects disappeared beneath it. A second,
perhaps more debilitating, problem was that the labels on collections often displayed too
few characters to be recognizable. Several examples of these clipped labels can be seen
in the overview in Figure 4. This type of clipping on labels was implemented so that
objects would use the same proportion ofspace at allzoomlevels. Ifthis proportional
space constraint was not enforced, objects would begin to overlap as the space became
more crowded. For example, if the "Young or New Drivers" collection label in Figure 4
were not clipped to the collection boundaries then it would overlap the "Non-Autos"
collection, potentially reducing readability and accessibility of one or both collections.
50
Moreover, extending collection labels beyond the collection boundaries tends to reduce
the value of spatial location in the overview since label bounds would not correspond
directly to collection bounds. Henderson et al. arrived at similar conclusions concerning
a spatial overview in developing Rooms [51].
2.5 Third Design Iteration - Niagara version 3
From the preceding study, a tradeoff in the design of the overview was identified.
The two primary choices for the overview are a geometrically reduced representation of
the workspace or an abstracted representation of the structure of the space. Geometric or
spatial overviews are popular in image editing software and map based systems while
abstract or structural overviews are popular in presentation editors or hierarchical
browsers such as file systems. The spatial overview has the advantage that it preserves
many of the geometric properties of objects in the workspace including size, distance,
and position. However, spatial overviews sacrifice much of the value of non-geometric
object properties such as textual labels on collections in the space. More generally, the
utility of the spatial overviews is fundamentally constrained by physical properties of the
workspace including size, space population, and aspect ratio since these properties limit
the levelofdetailthat can be shown in the overview. In contrast, the structuraloverview
has the opposite properties in that it preserves the non-geometric properties like text
labels at the cost of size, distance, and position. Because structural overviews are not tied
to geometry, their utility does not depend on the physical properties of the workspace.
Because of these tradeoffs, the third round prototype replaced the spatial overview
withastructuraloroutlineoverview(seeFigure1).Sincetheoverviewwaslargely
intended to provide access and readability of collection labels, a structural overview or
51
outline view seemed to offer a more useful layout for text labels than a spatial overview.
This is particularly true for nested hierarchical collections since a spatial overview could
only show collections at a single level in the hierarchy while an outline view could show
several levels of nesting at once.
Although these latest changes to the overview are likely to improve Niagara's
usability for grouping tasks, they still do little to address the fundamental problem of
limited screen space. As a result, a new technique was added to Niagara called automatic
text reduction to create more meaningful representations of text objects under zooming to
increase Niagara?s usable screen size. The automatic text reduction technique, shown in
Figure 6, combines font size reduction and content reduction to decrease the space
requirements of a text object while preserving the legibility of key words. In other
words, when the user zooms out to see more objects at once, automatic text reduction
tries to keep some of the text readable for the zoomed out objects. Because users have
often explicitly incorporated the text objects into the workspace, they are usually familiar
with the content of each object. As a result, the purpose of text reduction is primarily to
allow users to recognize the text objects they incorporated into the workspace earlier. In
addition to increasing usable screen space, automatic text reduction has the potential to
assist users in abstraction. Using content reduction techniques such as eliminating
common words may help users to more easily identify patterns such as rare, recurring key
words or related concept terms.
52
Figure 6: An example of automatic text reduction applied to an object at several different
zoom levels.
A second feature introduced in Niagara to make text objects more usable in the
zoomable space is automatic grouping. Automatic grouping assigns objects to groups
when the user moves multiple objects close to one another. The system then indicates the
object's assignment to a group by automatically changing its background color to match
the other objects in the group. The groups are just as quickly dismantled when the user
the drags the objects out of the group. One of the primary advantages of these groups is
that they have a consistent coloring that is easily recognizable when the view is zoomed
out. In addition, the system also provides facilities for operating on the automatically
created groups as a whole. The user can move the groups as a single object via a group
handle. Likewise, an iconic representation of the groups appears in the structural
overview ofthe space to allow for rapid editing ofor navigation to the group. These
groups can also be given a label to further identify them as the contents become more
stable. Labeled automatic groups are shown in Figure 7.
The plasma membrane is the edge of life,
the boundary that separates the living cell
from the nonliving surroundings.
plasma membrane boundary
separates nonliving surrounding
The plasma membrane is the edge of life,
the boundary that separates the living cell
from the nonliving surroundings.
plasma membrane boundary
separates nonliving surrounding
plasma membrane
-
-
-
-
53
Figure 7: A screenshot of automatically created groups in Niagara.
One final, more standard, feature added to improve the usability of zooming in
this version of Niagara was full text tooltip popups, shown in Figure 8. These work just
like standard WIMP tooltips by showing the full text of an object in a transient window
when the mouse is positioned over a zoomed out text object. The primary difference
these popup texts have fromtraditionaltooltips is that theyappear without requiring a
pause when the user moves the mouse over an object in order to accelerate reading
multiple items. The normal tooltip pause was eliminated in order to allow users to
quickly access the full text of an object for fast comparisons between object. A similar
feature is implemented in PhotoMesa [6], a zoomable photo browser, to show the photo
currently under the cursor.
Figure 8: A screenshot of the full text tooltips provided in Niagara.
54
2.6 Studying a Simplified Task
There are a number of challenges in designing studies to evaluate the technology
in an authoring system like Niagara. Perhaps the biggest challenge is that text-based
authoring tasks take a long time to complete, primarily because of the large amount of
information that needs to be read and understood. This means that experimental subjects
can get tired, bored, or distracted in completing the tasks and therefore exhibit
inconsistent behavior. Long task completion times also make experiments less viable
both for participants and administrators. Likewise, these tasks involve many cognitive
operations including reading, abstracting, comparing, searching, group-labeling, etc. As
a result, there can be a lot of variability in the way subjects combine these different
operations to complete the tasks. An additional problem is that many text-based
organization tasks do not have a single correct answer. This makes it difficult to
consistently evaluate many users? output from a particular task.
Because of these difficulties, a simpler task was needed that would retain the
defining characteristics of the text-based organization task. In a text-based organization
task, the organizer puts objects into groups based on common topics. The challenge in
creating these groups is that each text object has many different topics at multiple levels
of abstraction. As a result, each object can potentially fit into many different groups.
The ultimate goal is to find the most strongly related groups of objects while respecting
constraints such as limiting group size and balancing the number of objects between
groups.
To simulate this text organization task, a simplified task was created that
substitutes shapes for text objects. Similar to the task with text objects, the simplified
55
task requires subjects to sort shapes into groups where objects in the groups are strongly
related by visual properties. Because subjects would not be told the common properties
for each group, the task would primarily require subjects to look for the strongly related
groups. The task would start with a random arrangement of shapes and would be
completed when the shapes had been sorted into a specified number of equal-size groups.
The particular instance of this task used in our experiments involves finding
groups with two common properties. For example, the completed task might have one
group containing circles with green backgrounds, another group containing shapes with
blue backgrounds and pink borders, etc. An example of this task can be seen in Figure 9.
Just as with the text objects, the shapes have a number of properties on which they can be
compared including shape, background color, background texture, border color, border
texture, and size. Each of these six properties has six values leading to 6
6
or 46,656
different possible shapes. The total number of different group membership criteria is (6
choose 2) x (6 choose 1) or 90 different criteria.
Using shapes instead of text reduces the amount of information involved in the
task which in turn reduces task completion times. This simplification also reduces the
number of cognitive operations needed to complete the tasks, therefore reducing
variability. Further, because shapes can have well defined visual properties, they
facilitate a quantitative measure of the quality of results.
56
Figure 9: An example shape grouping task. One group has grouping criteria of squares
with blue borders. The second group has grouping criteria of diagonal striped textures
and thin borders.
Admittedly, there are differences between the shape task and text task. In
particular, because it uses perceptual properties such as color, size, shape, and texture,
this shape task introduces the issue of pre-attentive versus conscious processing.
However, since task completion times were expected to be on the order of fifteen minutes
for these tasks, the effects of the different kinds of processing were likely to be
insignificant. The tasks also differ in their use of verbal versus visual working memory.
This means that subjects may be able to hold different numbers of objects in memory in
shape tasks versus the text tasks (for example, see discussion in [88]). However, the text
tasks often have additional complexities, for example many non-topic words, that limit
the number of text objects that can be held in memory at once. Most importantly, it is
57
expected that any difference in cognitive processing would affect all tools equally. This
would likely mean a difference in the scale of the effects found between different kinds
of data.
From previous experience, the hypothesis was developed that ZUIs would
improve performance over other available interface paradigms for organization tasks.
The reasoning behind this hypothesis is discussed in more detail below. Based on this
hypothesis, the study below was designed based on the shape task to compare a ZUI to a
folder-based interface. Only after a positive difference was found for the simplified
shape task would the study be repeated for the original text task. It also anticipated that
in studying this simplified version of the task, a better understanding of the text task
could be developed.
2.6.1 The Core Question: Zooming versus Folder Interfaces
As previously mentioned, computer users often organize information using spatial
arrangements. Yet most computer displays are only large enough to show the equivalent
of one or two pages of information at normal size. Since this is a significant limitation
relative to physical surfaces such as tables or whiteboards, computer interfaces to support
organization usually implement mechanisms to increase the size of the available virtual
space.
One type of interface used to increase the virtual space on a computer display is
folders. Folders are an example of a more general technique that is analogous to
"paging" in a physical book. Paging provides access to a large workspace that is divided
into two-dimensional pages. On a computer, these pages are typically accessed by the
user via an overview such as a hierarchy of labeled folders. Examples of tools that use
58
such a paging mechanism are electronic books, presentation slide authoring tools, and file
system explorers.
Another approach is to have one big space with a navigation mechanism. Perhaps
the simplest navigation mechanism is scrollbars. Scrollbars are ideal for long one-
dimensional workspaces such as paginated text documents. For these spaces, a scrollbar
presents an overview of the entire space and facilitates rapid systematic navigation of all
content. Scrollbars become much less effective when extended to two dimensions. Here
the horizontal and vertical dimensions are each assigned their own independent
scrollbars. As a result, the two individual scrollbars do not represent an overview of the
entire workspace; instead they represent linear strips of the workspace. This means that
to perform a systematic navigation of the entire workspace, the author must coordinate
movements of both scrollbars. Beyond the cognitive complexity of managing the
coordination, the independent scrollbars introduce additional pointing tasks to acquire
and operate both scrollbars.
A second example that combines a large workspace with a navigation mechanism
is ZUIs. In a ZUI, different views of the workspace are obtained by changing the
position and magnification in the space through mouse or keyboard operations. These
types of interfaces are often used in such settings as map-based systems, image editors,
and games.
A third type of interface used to increase available space is distortion-based or
fisheye techniques. For spatial arrangement tasks, distortion is often not desirable as it is
likely to interfere with users' perception of spatial relationships. As result, distortion-
based approaches were not explored here.
59
The two specific interfaces compared here are a folder-based interface and a ZUI-
based interface. An informal analysis of users' presentation organization behavior is
presented next to highlight the difficulties a user encounters in organizing information
using folders. This analysis was suggested by the work of Henderson, et al. [51], who
analyzed user behavior in a windowed computer environment using the language of
working sets from the virtual memory literature.
The assumptions of most virtual memory strategies are that programs make use of
limited size working sets and have locality of reference [51]. This basically means that
only a limited amount of information is needed in memory at once. When these
assumptions are violated, memory management strategies (not coincidently called
paging) lead to thrashing where a large percentage of time is spent managing memory
allocation rather than doing actual work.
Humans also manage memory when organizing information for a presentation.
One of the primary reasons for using a workspace for authoring presentation content is
because it functions as a large external memory. Yet the assumptions of virtual memory
strategies described above do not necessarily hold for this task. Indeed, rather than
locality of reference, the task often requires the user to access objects throughout the
external workspace. Non-local access is needed to support making comparisons between
objects in different parts of the workspace. These global comparisons allow a person to
find similarities between objects in different groups and mentally experiment with
alternate organizations.
Consequently, a user's ideal external memory for this task is a large two-
dimensional workspace, such as whiteboards or tabletops. The large workspace reduces
60
the cost of accessing items in the external memory since the entire workspace is visible
through fast eye and head movements. This allows the author to quickly compare objects
both locally within groups and globally between groups.
A folder-based interface seems to be less suited as an external memory for this
type oftask. Authors working with folders tend to associate one folder with one semantic
group. Instead of comparing items between groups with a fast visual scan as they would
in a large workspace, users of folders must initiate a page change, reorient to the new
page, and then make a visual scan. As a result, it is expected that the time needed to
compare items between groups would be substantially increased with folders over a
single large workspace. A countervailing factor is that users have previous experience
with folder-based interfaces for email and file systems. This raises the issue of whether
prior experience will lead to superior performance with folders and mask out the
expected benefits of a zooming interface. To resolve these questions, an empirical
evaluation is presented below for the hypothesis that a ZUI-based interface will have
shorter completion times than a folder-based interface for a shape organization task.
2.6.1.1 Method
2.6.1.1.1 Participants
Fourteen regular computer users participated in the study. Five of the subjects
were female and nine were male. Subjects ranged in age from 18 to 50 and were given a
gift certificate for their participation in the study.
2.6.1.1.2 Equipment
The study was run on Pentium 3 and Pentium 4 machines (ranging from 700 MHz
to 2GHz) with at least 256 MB of memory and 1600x1024 flat panel displays.
61
2.6.1.1.3 Procedure
For our within-subjects study, subjects were asked to complete two shape
grouping tasks using the two interface conditions of zooming and folders. The primary
interface metaphor for grouping the shapes in both interface conditions was simple drag
anddropofshapes.
The folder interface condition mimicked a file browser with a folder hierarchy in
a resizable pane on the left side of the screen and a freeform 2D workspace in a pane on
the right side of the screen. Each folder in the hierarchy provided access to its own
(conceptually) infinite non-zoomable 2D space that was made visible in the right hand
pane when the folder was selected. This 2D space allowed at least 10 shapes to be visible
at once and provided scrollbars when shapes in the workspace extended outside the
current view. The interface also provided facilities to create, name, rearrange, and delete
folders. When objects were moved between folders, the tool automatically positioned
objects as close to the center of the folder?s space as possible without overlapping other
objects. The folder interface is shown on the left in Figure 10.
Figure 10: The starting state for the two interface conditions in the shape study. The
folder interface is on the left and the ZUI interface is on the right.
62
The zoomable interface condition provided a freeform 2D workspace as in the
previous condition, but instead of a folder hierarchy, it provided mouse interactions to
zoom in and out in the workspace. Here again, scrollbars were provided when shapes
extended outside the current view, for example when the view was zoomed in. The
zoomable interface is shown on the left in Figure 10. The shapes in this zooming
condition also implemented a type of semantic zooming (semantic zooming is
summarized in [85]) that tried to visually preserve shape properties. So while the
absolute size of the shapes was affected by zooming, the other properties, including a
shape's size relative to other shapes, were largely unaffected. One primary exception was
that background color and pattern sometimes became difficult to see as the view was
zoomed out and border widths increased relative to the size of the objects. Similarly, as
the view was zoomed out the relative differences in size between shapes became less
apparent. This type of problem required participants to zoom in to resolve any
ambiguities.
In both interface conditions, the system implemented a simple form of "bumping"
to prevent objects from occluding one another. Because this kind of bumping is difficult
to implement for arbitrary shapes, the system implemented an approximation of the
optimal bumping behavior. Although there were cases where this algorithm defaulted to
bumping with the bounding boxes, in practice it gave reasonable results for the most
common cases. While occlusion is not particularly disruptive for shapes, it is likely to be
more disruptive for more abstract object types such as text, web thumbnails, or pen input
[93]. In addition, bumping effectively increased the demand for space, which is likely to
highlight differences between the two interfaces. A similar effect could have been
63
achieved by increasing the number of shapes in the tasks, although this would have also
increased completion times.
The study task involved grouping shapes based on common properties. The
shapes had six properties (see above) that were described to the subjects prior to
completing the tasks. For each task, subjects were asked to identify groups of 10 shapes
that had two of the six properties in common. These two properties were not specified to
the subjects ahead of time as a primary component of the task was intended to be the
discovery of the group membership criteria. A sample task is shown in Figure 9.
Subjects were informed that a unique set of property pairs divided the objects into groups
of 10 but that individual objects may fit into more than one group.
There were several considerations in choosing the size of the shapes used in the
study. Since the study was ultimately simulating tasks involving text objects, the shapes
were chosen to approximate the size of a typical text object. In Niagara, a fairly typical
text object with 25 words at a 12 point SansSerif font with an average word length of 5
letters has a bounding box of 200 x 90 pixels. However, the shape task had an additional
constraint since size was one of the properties that varied between shapes. As a result it
was necessary for subjects to be able to clearly differentiate between differently sized
shapes when zoomed in. This required particular attention for comparing the sizes of, for
instance, diamonds and squares. Informal evaluation of different variations in shape sizes
led to a difference of 40 pixels between sizes. A minimum size constraint of
approximately 125 x125 pixels was enforced for the shapes so that subjects would be able
to clearly identify the background textures for all the different background texture-border
type-shape combinations when zoomed in. The combination of these different
64
constraints led to an average bounding box of 205 x 205 pixels for the shapes in the
study. It should be noted that although this results in a larger bounding area than with the
text objects, several of the shapes (i.e. star, hexagon, triangle, circle, and diamond)
covered only a fraction of the area in their bounding box. Ultimately, it is expected that
the average shape area is comparable to a typical text object?s area.
For each condition, subjects were first given a tutorial on how to use the interface.
The task was then explained and the six shape properties were described. Subjects were
told to indicate grouping through spatial proximity in the zooming condition and with
folders in the folder condition. The experimenter demonstrated a sample task using the
current interface, and the subjects were then given their own practice grouping task to
complete. Finally, the subjects were given the actual timed task and were told to complete
the task as fast as they were able.
At the beginning of each task, the shapes were distributed in a random order. For
the zooming condition, the shapes were distributed in a single large grid. In the folder
condition, the shapes were distributed in folders such that each folder initially contained
10 objects. The practice tasks were composed of 30 objects requiring 3 groups of 10 and
the actual tasks were composed of 50 objects or 5 groups of 10. The ordering of the
interface condition, practice task, and actual task were all counterbalanced to reduce
ordering effects.
There was a tradeoff in the design of the starting condition for the folders task.
Instead of distributing the shapes into the 5 folders, shapes could have been put into a
single vertically-scrollable root folder to start with. This would have the advantage that
all objects would be in a single conceptual space to start with. However, because the
65
screen could show approximately 10 shapes at a time, the subject would have to scroll
through approximately 5 screens worth of shapes at the beginning of the task. It was
ultimately decided that the 5 folder icons provided faster and more consistent access to
the shapes. Subjects could quickly see all 50 objects with 5 clicks on the folder icons.
Moreover, they could directly jump to a consistent set of 10 objects with a single click.
It should also be noted that folders did not have to be used with the folder-based
interface. Although subjects were asked to put their final group selections into individual
folders, they were free to move all the objects into a single folder during the task, using
the scrollbars instead of folder icons to navigate. Because the tool automatically
positioned objects as close to the center as possible, subjects could quickly move the
objects into a single folder and get a well-packed space in which to scroll around. The
time needed to move all the objects into a single folder was nearly insignificant relative
to the overall task time. In the experiment, one subject was observed to use this strategy.
The software logged all relevant operations, though this information was
ultimately not analyzed. Following the two tasks, subjects were given a questionnaire
regarding their experiences with the two conditions. The questionnaire is available in
Appendix A.
2.6.1.2 Results
Paired two sample t-tests were used to analyze the results from the study. These
were chosen over ANOVAs because only one independent variable and two samples
were being compared.
66
One subject's results were discarded because of several significant cell phone
interruptions during the timed tasks. The means for the dependent variables are shown in
Table 4 for the remaining 13 subjects. A paired two sample t-test was performed for task
completion times. This data indicated a statistically significant effect of interface type p
< 0.03 with a 30% faster completion time for zooming. The mean completion times,
shown in Figure 11, were 17.5 (s 8.0) minutes for folders and 12.2 (s 7.0) minutes for
zooming.
Figure 11: A graph comparing the mean completion times for zooming versus folders.
The error bars indicate standard deviation.
There was also a statistically significant effect of interface type on two
questionnaire items. For the question "What is your overall reaction to using this tool for
the task?" (1=Frustrating,5=Satisfying) the means were 4.2 (s 0.6) for zooming and 2.8
(s 1.0) for folders with p < 0.001. For the question "How often did you feel like the
17.47
12.24
0
5
10
15
20
25
30
Zooming Folders
Ti
m
e
(m
i
n
)
67
software interrupted your thinking?" (1=Rarely,5=Often) the means were 1.8 (s 0.9) for
zoomingand3.4(s 1.3) for folders with p < 0.001.
Table 4: The means and standard deviations of dependent variables measured in the
study.
Zooming Folders
Completion Time (min.)
12.2 (s 7.0) 17.5 (s 8.0)
Satisfaction
(1=Frustrating, 5=Satisfying)
4.2 (s 0.6) 2.8 (s 1.0)
Interrupted
(1=Rarely, 5=Often)
1.8 (s 0.9) 3.4 (s 1.3)
2.6.1.3 Discussion
The results indicate that a zoomable interface has a considerable advantage over a
folder interface for the shape grouping task. A number of factors likely contribute to this
advantage but the most notable seems to be the time involved in making comparisons
between shapes. In the zooming condition, comparisons were usually made with rapid
eye and head movements that took a few hundred milliseconds. In contrast, the folder
condition often required subjects to look at objects in multiple folders. Moving between
folders introduced a mouse pointing operation increased the overhead of many
comparisons to more than a second. Because of the large number of comparisons
involved, this relatively small difference in time became significant over the complete
task.
A second possible contributing factor was the formality of folders versus spatial
arrangement. Using the folder interface, subjects would often mark a folder with a
descriptive name or unique position to indicate that the contained group was completed.
Informal feedback from the study indicated that this behavior provided subjects with a
68
sense of orderliness and progress. However, subjects were also observed to be hesitant to
revisit groups that had been marked as complete. As a result, this behavior often delayed
their completing the task when these groups contained an error. This problem was less
frequently observed with spatial arrangement in the zooming condition, suggesting that
the formal separation of groups in the folder condition is harmful for maintaining
awareness of groups once the user considers them complete.
2.6.2 Scaling Effects and Other Parameters of Task Difficulty
The previous study demonstrated the benefits of ZUIs for novice users performing
an organization task with a modest number of shapes and fixed organization parameters.
It does not address how folders and ZUIs differ for more experienced users, how user
performance changes as the number of objects to be organized increases, or what
parameters most affect the task?s difficulty. Performing additional controlled studies to
examine each of these issues would be extremely time-consuming. As a result, what are
presented in this section instead are multiple aggregated trials performed by a single
expert user. Although these results must be regarded cautiously because of the small
sample size, they can indicate potential trends and possible areas for further research.
Of course, understanding the difficulty of shape tasks is not the ultimate goal
here, rather these results are intended to be suggestive for text based organization tasks.
The implications of these results for text organization will be discussed in the next
section following a controlled study of text organization.
There are many parameters that can influence task difficulty for the shape
organization task. The task parameters explored here include:
69
1) Number of Objects ? this determines the overall scale of the task. In addition, the
number of objects is likely to interact with the kind of tool being used to perform the
task. In particular, task completion times with folders seem likely to degrade
rapidly since the percentage of objects that can be viewed at once in a folder
becomes smaller and smaller as the number of objects increases.
2) Number of groups ? given the number of objects in the entire task, the number of
groups determines the number of objects in each group. The task described in the
study had 5 groups; hence there were 10 objects per group.
3) Group membership criteria - the group membership criteria are the number of object
properties that are used to define group membership. The task described in the
study had two membership criteria, for example, circles with red backgrounds or
objects with blue backgrounds and green borders. Increasing the number of
membership criteria to three would lead to groups such as circles with red
backgrounds and blue borders or squares with checkered backgrounds and thin-
dotted borders.
4) Overlapping membership criteria - the number of overlapping group membership
criteria indicates how many groups have the same values as membership criteria.
For example, there is one overlapping set of membership criteria when one group
has circles with red background and another group has circles with green borders.
This parameter was not explicitly controlled for the tasks in the previous study.
However, the two tasks that were actually used in the study were similar in this
regard.
70
5) Total object attributes ? the totalnumber ofobject attributes is the number of
attributes that vary in a set of objects. For instance, if a set of objects varies on the 3
attributes of shape, background color, and border color, then all the objects in the
group would be identical except for these three attributes. The task in the previous
study had 6 total attributes.
6) Possible values per attribute ? the possible values per attribute is the number of
different assignments there are for a particular attribute. For instance, if a color
attribute has values of red, blue, and green, then there are 3 values for that attribute.
In the previous shape task, the attributes each had 6 possible values.
As mentioned, an experienced shape organizer (specifically, the author)
performed a suite of shape organization tasks that varied each of the above parameters
one at a time. The shapes used in these tasks were randomly generated by a computer
program within the constraints of the different parameter settings. Since the program
randomly assigned the values for the different shape attributes, the organizer was not
aware of the final answers prior to completing the tasks.
The organizer performed six trials for each combination of parameter settings.
One exception was that only three trials were run for each of the tasks involving 120
objects since these trials often took more than 25 minutes to complete. The points on the
graphs below indicate the means of the 6 trials and error bars indicate the standard
deviations for these trials. Note that some trial sets were reused between comparisons as
theyhad the same parameter settings.
The first set of trials shown in Figure 12 varied the number of objects to be
organized. Shapes had to be put in to groups of 10 for each of these trials. The total
71
number of attributes, number of membership criteria, and possible values per attribute
were held constant for each of these trials at 6, 2, and 6 respectively. Overlapping
between membership criteria was not controlled. The trials were performed for both
zooming and folders.
The data in Figure 12 indicates that the completion times are increasing at a more
rapid pace in the folder condition than in the zooming condition. Fitting a power
regression equation to the data for the folder condition yields y=0.0006x
2.312
with R
2
=
0.987. Likewise for the data in the zooming condition, fitting a power regression
equation to the data yields y=0.0006x
2.216
with R
2
= 0.983. In contrast, fitting an
exponential regression equation to the folder and zooming data yields y=0.712e
0.035x
with
R
2
= 0.977 and y=0.518e
0.0329x
with R
2
= 0.934 respectively. More data would be needed
to accurately predict whether the completion times are increasing at an exponential or a
polynomial rate. Regardless of the underlying functions, the existing data suggest that
zooming is going to outperform folders for any number of objects.
Overall, the rapid increases in completion time are predicted by several inherent
challenges in the task as the number of objects increases. First, the potential for
unintended, random groups increases with the number of objects. These unintended
groups lead to false starts and dead ends. For example, when a group of objects fits a set
of unintentional membership criteria, putting them into a group is likely to prevent the
organizer from completing 1 or more other groups. This means that the organizer must
perform a substantial reorganization before the task can be completed.
72
Figure 12: Graph of trials that varied in number of objects.
Similarly, increasing the number of objects also increases the chances that objects
will unintentionally fit into more than one group. For example, one group might have
membership criteria A and B, while a second group has membership criteria C and D. As
the number of objects increases, the probability that there will be objects matching all
four of these criteria also increases. When these ambiguities occur, the organizer will
often end up with too many objects in one group and not enough in another group. In this
case, the organizer must revisit groups and reevaluate membership criteria in order to find
the ambiguous objects.
Another important factor that affects task completion times is the ratio of group
size to overall number of objects. This ratio decreases as the group size is held constant
(as it was here) and the number of objects increases. As the ratio decreases, the similarity
Number of Objects
0
10
20
30
40
50
60
0 20 40 60 80 100 120 140
Tim
e
(
m
in
)
Zooming
Folders
Tim
e
(
m
in
)
Tim
e
(
m
in
)
73
of the group members is less prominent as the number of objects outside the group with
overlapping properties increases.
Figure 13 shows the second set of trials that varied in the number of groups. The
trials consisted of 50 shapes and all trials were performed under the zooming condition.
The trials used 2 group membership criteria, 6 total attributes, and 6 values per attribute.
Overlapping between membership criteria was not controlled for these trials.
Figure 13: Graph of trials that varied in the number of groups.
The graph indicates that completion times are proportional to the number of
groups. This is partially because as the number of groups increases, the group size
decreases which increases the probability that a set of objects will unintentionally have
the necessary number of properties in common. Similarly, as the number of groups
increases, the probability increases that objects will unintentionally fit into more than one
group. In fact, the number of group-to-group relationships is increasing quadratically
0
2
4
6
8
10
12
14
16
18
24681012
Number of Groups
Tim
e
(min
)
Tim
e
(min
)
74
with the number of groups that suggests a potential quadratic increase in completion
times.
The third set of trials shown in Figure 14 varied in the number of group
membership criteria necessary for each group. The trials consisted of 50 shapes which
still had to be put into groups of 10 for each of these trials. The total number of attributes
and possible values per attribute were held constant again for each of these trials at 6 and
6 respectively. Overlapping between membership criteria was not controlled. The trials
were all performed under the zooming condition.
Figure 14: Graph of trials that varied in the number of group membership criteria.
The data in Figure 14 indicates that completion times are inversely proportional to
the number of group membership criteria. This is because as the number of criteria
increased, the probability that objects would unintentionally have all the criteria in
common decreased. As a result, fewer similar objects were needed to identify the
0
2
4
6
8
10
01234
Group Membership Criteria
Ti
m
e
(
m
i
n
)
Ti
m
e
(
m
i
n
)
75
grouping criteria for a particular group since the number of similarities was greater.
Trials were stopped beyond 3 criteria because the completion times had reached the
minimum time required to move the objects into groups.
The fourth set of trials shown in Figure 15 varied in the number of overlapping
membership criteria. For these trials, there were 50 shapes, the shapes needed to be put
into groups of 10, the total number of attributes was held at 6, the number of membership
criteria was held at 2 per group, and the number of possible values per attribute was held
at 6. These trials were all performed under the zooming condition.
Figure 15: Graph of trials that varied in the number of overlapping membership criteria.
The data in Figure 15 suggests that the relationship between completion times and
overlapping membership criteria is somewhat complex. The overlapping membership
criteria seemed to interact with one of the initial challenges in the shape task, namely
identifying possible group criteria. When the number of overlapping criteria reached 2,
0
1
2
3
4
5
6
7
012345
Number of Overlapping Membership Criteria
Ti
m
e
(
m
i
n
)
Ti
m
e
(
m
i
n
)
76
the search for promising group criteria became easier since several groups of objects
shared a single criterion. However, this identification problem only occurs at the
beginning of the task since as the task goes on, there are fewer objects in which to look
for patterns. As a result, the advantage with 2 overlapping membership criteria was
overwhelmed by the problems introduced by the overlapping groups. Specifically,
having overlapping groups meant that many objects fit into multiple groups. These kinds
of ambiguities required additional time to resolve.
Figure 16 shows the data from the fifth set of trials that varied the total number of
shape attributes. These trials had 50 shapes, 10 shapes per group, 2 membership criteria,
and 6 values per attribute. Overlapping between membership criteria was not controlled.
These trials were all performed under the zooming condition.
Figure 16: Graph of trials that varied in the total number of shape attributes.
0
1
2
3
4
5
34567
Total Number of Shape Attributes
Ti
m
e
(
m
i
n
)
Ti
m
e
(
m
i
n
)
77
The graph in Figure 16 indicates that completion times increase in proportion to
the total number of attributes. This is perhaps the most intuitive of the results since the
conceptual search space increases as the total number of attributes increases. Restated, as
the conceptual space expands but the number of similarity group criteria stays the same,
the relative similarity of the objects decreases. This ultimately makes finding the
membership criteria more difficult for the organizer.
Figure 17 shows the final set of trials that varied in the number of possible values
per attribute. These trials had 50 shapes, 10 shapes per group, 2 membership criteria, and
6 toal attributes. Overlapping between membership criteria was not controlled. These
trials were all performed under the zooming condition.
Figure 17: Graph of trials that varied in the number of possible values per attribute.
0
2
4
6
8
10
12
14
16
18
234567
Possible Values Per Attribute
Tim
e
(
m
in
)
Tim
e
(
m
in
)
78
The graph in Figure 17 indicates the complex relationship between the values per
attribute and completion times. When there are only 2 values per attribute, there are
many unintentional groups. In fact, there are so many unintentional groups that it
becomes possible to complete the task without finding the intended groups. With 3
values per attribute, the task becomes much more difficult. Although there are still many
unintentional groups, it is typically no longer possible to complete the task unless the
intended groups are found. As a result, the organizer creates many groups that they must
later abandon when they prevent the task from being completed. At 4 attributes, the
graph becomes more regular. Here, increasing the number of values decreases the
probability that an object will be randomly assigned one of the values being used as
group membership criteria. In an extreme example, if there are 1 million values for a
particular attribute and one of the values is used as a group?s membership criteria, then
there is an extremely low probability that an object that is not a member of the group will
be randomly assigned the value used by the group.
2.6.2.1 Discussion
Ultimately, each of the parameters explored above is affecting the similarity of
group members relative to the other objects outside the group. As a result, the data seems
to suggest that several of these parameters are not independent. For example, in Figure
12 the number of objects to be grouped is also increasing the number of groups since the
number of objects per group is held constant. Comparing completion times between the
group-size data in Figure 13 and the number of objects data in Figure 12 indicates similar
results when the number of groups is the same. Specifically, completion times with 100
objects and 10 groups in Figure 12 averaged 13.69 min whereas completion times with
79
50 objects and 10 groups in Figure 13 averaged 15.47 min. This suggests that the number
of groups may be a better predictor of task difficulty than the raw number of objects.
Similarly, increasing the total number of attributes in Figure 16 also increases the
ratio of total attributes to the number of group membership criteria. Comparing
completion times between the total attributes data in Figure 16 and the number of group
membership criteria data in Figure 14 indicates similar results when the ratio of the
number of membership criteria to total attributes is the same. In particular, completion
times with 3 membership criteria and 6 total attributes in Figure 14 averaged 2.22 min
and completion times with 4 total attributes and 2 membership criteria in Figure 16
averaged 2.45 min.
Taking into account these dependencies, the results suggest the following general
relationships between task parameters and task difficulty:
1) Difficulty increases with the number of groups
2) Difficulty increases with the ratio ofthe totalnumber ofattributes to the number of
membership criteria
3) Difficulty decreases with the number ofvalues per attribute
4) Difficulty increases with the number of overlapping membership criteria
Following the study in the next section is a discussion of the extent to which these
relationships apply to text-based organization tasks.
2.7 Extending Simplified Task Results to Text
Although the shape task simulates the text grouping task in manyways, it also
differs in several important aspects. Consequently, the results in the previous section
80
require further validation in order to be applied to text. The most significant interface
difference between the two tasks is that shapes retain nearly all their value when they are
viewed zoomed out. In contrast, text objects quickly become unreadable when they are
viewed zoomed out. Automatic text reduction is an attempt to improve the
representations of arbitrary text objects under zooming. Still, this technique is not going
to preserve all of the value of a text object under zooming since it removes some content
from the objects, sometimes missing the complications and subtly of human language.
The shape task further differs fromthe text grouping task in that text objects do
not have well defined properties whose values can be compared. As a result, part of the
task in grouping text objects is identifying the dimensions along which objects can be
compared. This identification is further complicated because text objects have differently
weighted topics. Topics are also hierarchical meaning that two different topics can be
related if they share a common abstracted parent topic. In contrast, the dimensions for
comparison in the shape task were un-weighted, well-defined, and provided to subjects at
the task's outset.
Our previous studies have suggested many difficulties involved in studying a text
based organization task in a controlled setting including: long completion times, large
variability due to the task?s cognitive difficulty, and a lack of quantitative measures of
results. In previous studies, it was also observed that the variability between subjects was
further aggravated because the tasks did not have clear stopping conditions. Quality was
often mediated by the amount of time subjects spent on the task.
In this section, a study of text organization is described that tries to replicate the
results found in the previous shape study. It is expected that there are not solutions to
81
many of the study-related problems described above since these problems are inherent to
text-based tasks. However, the study task does provide clear stopping conditions by
requiring subjects to create a specific number of fixed size groups. In addition, the
familiar task involving the driver?s handbook was chosen to reduce completion times.
2.7.1 Method
2.7.1.1 Participants
Fourteen regular computer users participated in the study. Five of the subjects
were female and nine were male. Subjects ranged in age from 23 to 62 and were given
snack food for their participation in the study. Two of the subjects in text study had also
participated in the shape study.
2.7.1.2 Equipment
The study was run on Pentium 3 and Pentium 4 machines (ranging from 1.2 GHz
to 2GHz) with at least 256 MB of memory and 1600x1024 flat panel displays. The mice
on these machines were also equipped with a scrollwheel.
2.7.1.3 Procedure
As in the shape study, subjects were asked to complete two organization tasks
using the two interface conditions of zooming and folders. All subjects used both
interface conditions so the study again had a within-subjects design. The primary
interface metaphor for grouping the objects in both interface conditions was simple drag
anddropofshapes.
The interface conditions used in the study were primarily the same as the shape
study. The folder interface condition mimicked a file browser with a folder hierarchy in a
82
resizable pane on the left side of the screen and a freeform 2D workspace in a pane on the
right side of the screen. Each folder in the hierarchy provided access to its own
(conceptually) infinite non-zoomable 2D space that was made visible in the right hand
pane when the folder was selected. This 2D space allowed at least 20 text objects to be
visible at once and provided scrollbars when text in the workspace extended outside the
current view. The mouse scrollwheel could be used to vertically scroll the workspace.
The interface also provided facilities to create, name, rearrange, and delete folders.
When objects were moved between folders, the tool automatically positioned objects as
close to the center of the folder?s space as possible without overlapping other objects.
The starting state for the folder condition in the study is shown on the left in Figure 18.
The zoomable interface condition provided a freeform 2D workspace as in the
previous condition, but instead of a folder hierarchy, it provided the ability to zoom in
and out in the workspace using the mouse scrollwheel. Here again, scrollbars were
provided when text extended outside the current view, for example when the view was
zoomed in. As previously mentioned, the text in this zooming implemented a form of
automatic text reduction. So while the bounding box of the text was affected by
zooming, some of the text was kept readable. Automatic grouping, described above, was
also provided in the zooming condition. This provided a means to delineate groups,
move groups as a whole, and give groups a label. Lastly, the zooming condition also
implemented tooltips, described above, that provided access to the full text of an object
when the view was zoomed out. The starting state for the zoomable condition in the
study is shown on the right in Figure 18.
83
Figure 18: The starting state for the two interface conditions in the text-based study. The
folder interface is on the left and the ZUI interface is on the right.
Because several unique features are included in the zooming condition, the study
does not control for the effects of these individual features. This study is instead intended
to identify any high order benefits of a well designed ZUI over a folder interface.
Additional studies would be needed to determine which ZUI features have the biggest
role in any overall benefits found.
As in the previous study, both interface conditions implemented a simple form of
"bumping" to prevent objects from occluding one another. Here again, bumping
effectively increased the demand for space, which was expected to highlight differences
between the two interfaces. A similar effect could have been achieved by increasing the
number of objects in the tasks, although this would have also increased completion times.
The studytask involved putting text into groups based on common topics. In
contrast to the shape study, subjects were asked to put objects into groups of 5 rather than
groups of 10. The smaller groups were chosen because the large groups would be too
84
easy for a text-based task and would not reflect the true difficulty of the task. The timed
tasks again consisted of 50 objects resulting in 10 groups of 5.
The text objects used in the study were facts taken from the California Drivers
handbook. This is expected to be a familiar topic for most of the subjects, though with
enough details to be representative of an actual organization task. The tasks used in the
study had an average of 21.3 words with an average of 5.5 letters per word. Text for the
objects was shown at a 12 point SansSerif font.
Text describing the task and interface conditions was presented to the subjects in
a 320x1024 window that was visible throughout the task. This meant that they completed
the study tasks using the display?s remaining 1280x1024 pixels. For both conditions,
subjects were first presented with a tutorial on how to use the interface. Then, for the
first condition only, subjects were given a single practice task consisting of 10 objects
that had to be put into 2 groups of 5. Finally, subjects were given the actual timed tasks.
Subjects were told that although the tasks were timed, they should try to find the best
possible groups, keeping in mind that the objects in the groups did not have to be
completely similar. Subjects were told to indicate their final groupings through
automatic grouping in the zooming condition and with folders in the folder condition.
The ordering of the interface condition and task were counterbalanced to reduce ordering
effects.
At the beginning of the timed task, the text objects were distributed in a random
order. For the zooming condition, the text objects were distributed in a single large grid.
In the folder condition, the objects were distributed in single vertically-scrollable grid in
the ?root? folder. The folder condition also provided 10 pre-created folders in which
85
subjects could put their final 10 groups. As in the shape study, there was a tradeoff here
in the design of the starting condition for the folders task. Instead of being distributed in
the ?root? folder, objects could have been distributed evenlyin folders as was done in the
shape condition. The reason the scrolling condition was chosen here over the folder
condition was because of the smaller group size for text rather than shapes. As a result,
distributing the objects into 10 groups would unnecessarily segment the text objects,
making comparisons unnecessarily difficult. Moreover, the text objects used here had a
slightly smaller bounding box than the shapes allowing about 20 objects to fit on screen
at once. This reduced the amount of scrolling needed in the single folder distribution,
making it further preferable to the multi-folder distribution for this task.
One feature that was implemented in the zooming condition that could have also
been implemented in the folder condition was counters in the group labels. Two subjects
in the ?labeled both? group specifically commented on this and manually added counters
to several of their folder labels. Nonetheless, it is expected that this feature would have
made little difference in the overall results since determining group counts was a
relatively fast operation already.
Again, it should be noted that folders did not have to be used with the folder-
based interface. Although subjects were asked to put their final group selections into
individual folders, they were free to keep objects in the root folder during the task, using
the scrollbars instead of folder icons to navigate. In the experiment, all subjects were
observed to use this strategy to some extent.
The software logged all relevant operations during the timed tasks. These logs
were analyzed to help explain results found from other measures. Following each of the
86
two tasks, subjects were given a questionnaire regarding their experiences with the tool.
One of the more unique questions, given immediately following each condition, asked
subjects to estimate how long they thought the task took them. In a study involving
various web browser tasks, Czerwinski et al. demonstrated a relationship between this so-
called ?subjective duration assessment? and task success rate [23]. This measure was
collected here with the hope that it would provide an implicit measure of user satisfaction
in completing tasks with each interface. The full post-task questionnaire is available in
Appendix A.
2.7.2 Results and Discussion
Each of the subjects? final 10 groups was rated based on quality by 2 judges.
Each of the groups was given a score from 1 to 5 based on the largest subset of related
items in the group. Since all the facts were related to driving and driving safety, the
groups had to have some additional topic in common in order to be related. Group labels
were not counted in the rankings since not all groups had them. The final quality score
was a number from 10 to 50. The average of the two judges scores were used in the
comparison below. An analysis of inter-rater reliability between the two sets of scores
found a linearly weighted Cohen?s Kappa of 0.71. Since this was above the generally
accepted reliability level of 0.70, the scored were not re-rated.
The means for the dependent variables measured are shown in Table 5.
Preliminary paired two sample t-tests were initially used to analyze the results from the
study. These were chosen over ANOVAs because only one independent variable and two
samples were being compared.
87
The preliminary paired two sample t-tests were performed for the dependent
variables of completion time, number of labeled groups, quality of groups, and the
questionnaire items involving subjective duration, subjective satisfaction, and subjective
confidence in groups. There was a highly significant effect of interface type on the
questionnaire item "What is your overall reaction to using this tool for the task?"
(1=Frustrating,5=Satisfying) at p < 0.003 with means of 4.21 (s 0.89) for zooming and
2.79 (s 0.89) for folders. There was also a significant effect of interface type on the
number of groups labeled with p < 0.05. The mean number of labeled groups was 4.86 (s
5.05) for zooming and 7.50 (s 4.15) for folders. Graphs of these two measures are shown
in Figure 19.
Figure 19: Graphs of the two measures on which interface type had a significant effect.
Thegraphontheleftshowsthesubjectiverankingsforthequestionnaireitem?Whatis
your overall reaction to using this tool for the task?? The graph on the right shows the
number of groups labeled in each condition.
4.86
7.50
0
2
4
6
8
10
12
Zooming Folders
G
r
ou
ps
La
b
e
le
d
4.21
2.79
1
2
3
4
5
Zooming Folders
Ra
ti
n
g
(1
-5
)
G
r
ou
ps
La
b
e
le
d
G
r
ou
ps
La
b
e
le
d
Ra
ti
n
g
(1
-5
)
Ra
ti
n
g
(1
-5
)
88
It was not surprising that the difference in task completion times was not initially
significant. Subjects appeared to have a wide range of computer skills, cognitive
abilities, problem solving strategies, and engagement with the task. Also important was
that completion time was not emphasized as heavily as it was in the shape study. This
was done on purpose since the grouping criteria with text were less clear and less
absolute. This meant that subjects could make a tradeoff between speed and quality in
creating their groups. Since the interface was anticipated to have less of an effect if
subjects were concerned with speed, we chose instead to emphasize quality in the task
description.
Table 5: The means and standard deviations of dependent variables measured in the
study, divided on the single within-subjects factor oftooltype.
Zooming Folders
Completion Time (min.)
18.57 (s 6.43) 21.20 (s 8.35)
Group Quality (10-50)
46.71 (s 2.71) 47.00 (s 2.31)
Number of Labeled Groups (0-10)
4.86 (s 5.05) 7.50 (s 4.15)
Raw Subjective Duration (min.)
16.93 (s 7.20) 19.50 (s 9.35)
Subjective Duration Difference
(min.)
-1.65 (s 5.20) -1.70 (s 5.61)
Subjective Satisfaction
(1=Frustrating, 5=Satisfying)
4.21 (s 0.89) 2.79 (s 0.89)
Subjective Confidence in Groups
(1=Not Confident, 5=Confident)
3.36 (s 0.93) 3.36 (s 1.15)
2.7.2.1 Group Labeling Strategy
As mentioned above, several differences were observed between subjects in how
they used the tools to solve the tasks. This was reflected in the preliminary analysis that
showed an effect of tool type on the number of groups labeled. In fact, three clear
categories of labeling strategies were evident in the data. Three subjects (two women and
one man) fell into a first category that did not use any labels. Four subjects (two women
89
and two men) fell into a second category that used labels only on their folders and none
of their zooming groups. The remaining seven subjects (one woman and six men) fell
into a third category that put labels on both their folders and their zooming groups. Table
6 shows the means for each of the dependent variables divided by labeling strategy.
Table 6: The means and standard deviations of dependent variables measured in the
study, divided on the within-subjects factor of tool type and the between-subjects factor
of labeling strategy.
No Labels Labeled Folders Labeled Both
Zooming Folders Zooming Folders Zooming Folders
Completion Time (min.) 15.15
(s 2.70)
25.46
(s 13.85)
13.77
(s 6.08)
20.93
(s 10.29)
22.79
(s 5.16)
19.53
(s 4.67)
Group Quality(0-50) 45.67
(s 5.01)
44.83
(s 2.57)
46.13
(s 0.85)
46.25
(s 1.85)
47.5
(s 2.38)
48.36
(s 1.68)
Number of Labeled Groups
(0-10)
0.0
(s 0.0)
0.0
(s 0.0)
0.0
(s 0.0)
9.50
(s 0.57)
9.71
(s 0.49)
9.57
(s 1.13)
Raw Subjective Duration
(min.)
13.33
(s 7.64)
21.67
(s
17.56)
11.50
(s 5.07)
16.25
(s 8.54)
21.57
(s 5.38)
20.43
(s 6.29)
Subjective Duration
Difference (min.)
-1.82
(s 6.07)
-3.80
(s 5.37)
-2.27
(s 2.45)
-4.68
(s 3.42)
-1.22
(s 6.55)
0.89
(s 6.07)
Subjective Satisfaction
(1=Frustrating, 5=Satisfying)
5.00
(s 0.00)
2.67
(s 0.58)
4.25
(s 0.50)
2.50
(s 0.58)
3.85
(s 1.07)
3.00
(s 1.15)
Subjective Confidence in
Groups
(1=Not Confident,
5=Confident)
3.00
(s 1.00)
3.00
(s 1.73)
3.50
(s 0.58)
3.50
(s 1.00)
3.43
(s 1.13)
3.43
(s 1.13)
As a result, the data was analyzed treating the number of labeled groups as a
between-subjects factor with the three conditions described above. This led to a series of
3x2 ANOVAs with labeling strategy as a 3 condition between-subject factor and tool
type as a 2 condition within-subjects factor. ANOVAs were used here in preference to t
tests since the results involved a complex combination of factors. These analyses
indicated a significant effect of tool type on completion time with F(1,11)=6.39, p < 0.03
and on the questionnaire item "What is your overall reaction to using this tool for the
90
task?" (1=Frustrating,5=Satisfying) with F(1,11)=18.24, p < 0.001. There was also an
interaction between tool type and labeling strategy affecting completion times with
F(2,11)=5.76, p < 0.02.
It is notable that all 3 groups of subjects showed a significant preference for
zooming4.21(s 0.89) over folders 2.79 (s 0.89) with the same average ratings as in the
shape study. In the shape study, the zooming condition was fairly intuitive and similar to
existing tools for working with visual data, such as map browsers or photo editors. In
this study, subjects were presented with the two completely unfamiliar interface
techniques of automatic text reduction and automatic grouping. Although this novelty
itself may account for some of the difference in preference, it seems unlikely that subjects
would show such a strong preference if the techniques did not provide some benefit for
the task.
A cursory look at the data across subjects is revealing. shows the patterns for the
four dependent variables of completion time, subjective duration assessment, subjective
satisfaction, and group quality. For each labeling strategy, the four graphs plot the
difference between measures for the two tool types. Although not all of the differences
shown in the graphs are significant, they are indicative of a consistent tendency between
the three labeling conditions.
In general, it seems very unlikely that the different labeling strategies are the
cause of the differences seen here. Looking in particular at the graph of completion
times, it seems unlikely that teaching users not to label their groups will make them more
efficient at the task with zooming. Instead, a more plausible explanation of these results
is that the labeling strategies reflect three different kinds of users. Because this task
91
heavily exercises short term memory and different spatial abilities, the labeling strategies
could be indicative of individual differences on these dimensions.
Figure 20: Graphs of the differences between four measures for each of the 3 labeling
strategies. The top left graph depicts completion time. The bottom left graph depicts
subjective duration assessment. The top right graph depicts subjective satisfaction. The
bottom right depicts group quality. Positive values represent higher values in the
zooming condition, while negative values represent higher values in the folder condition.
Our informal observations offer further evidence to suggest individual differences
between these three categories of users. Subjects who labeled in both conditions seemed
to be generally more comfortable with the computer and hence, more willing to explore
the nonstandard navigation techniques provided in the zooming interface. An analysis of
0
5
10
15
20
25
30
Zooming Folders
C
o
m
p
l
e
t
i
on
Ti
m
e
(
m
i
n
)
No Labels
Folders
Both
C
o
m
p
l
e
t
i
on
Ti
m
e
(
m
i
n
)
0
1
2
3
4
5
6
Zooming Folders
Ran
k
i
n
g
(
1-
5)
No Labels
Folders
Both
Ran
k
i
n
g
(
1-
5)
0
5
10
15
20
25
Zooming Folders
Es
t
i
ma
t
e
d
T
im
e
(
m
i
n
)
No Labels
Folders
Both
Es
t
i
ma
t
e
d
T
im
e
(
m
i
n
)
10
15
20
25
30
35
40
45
50
Zooming Folders
Q
u
al
i
t
y
(
10
-
5
0)
No Labels
Folders
Both
Q
u
al
i
t
y
(
10
-
5
0)
92
the operation logs from the software used in the study indicates that there is indeed a
difference in the number of navigation operations performed by these users under
zooming. This navigation measure combines zooming, panning, and scrolling and is
shown in Figure 21. A single factor between-subjects ANOVA indicates that these
results are not significant. Nevertheless, the general pattern further indicates that
individual differences, rather than labeling strategy, may be at the root of the
performance differences between the groups.
Figure 21: A graph of the number of navigation operations between the different labeling
strategies. The difference between strategies was not significant.
Users were also asked directly about their use of zooming and automatic text
reduction on the follow-up questionnaire. Ten of the fourteen subjects indicated that they
rarely or never used zooming during the task. This may at first glance seem surprising
since this was, after all, a zoomable user interface. However, this was actually a desirable
result since automatic text reduction and full text popups were added to increase the value
of zoomed out text objects and reduce the need for zooming. The responses to the
0
5
10
15
20
25
30
No Labels Folders Only Labeled Both
N
a
v
i
ga
ti
on
O
p
e
r
a
t
i
ons
93
automatic text reduction question further support this finding. Only three of fourteen
users indicated that they rarely or never made use of the automatically reduced text.
Interestingly, two of the three users who did not use the reduced text indicated a heavy
reliance on zooming. It is also possible that these three users were not aware that they
were using the reduced text since it was always visible and did not require any effort to
invoke.
Additional studies are needed to determine the specific cognitive mechanisms that
are driving these different labeling strategies. Regardless, the results indicate that some
categories of users are significantly faster at the task with zooming than with folders and
with no significant difference in quality. Since ZUIs are relatively novel, additional
training and experience with zooming could yield an even greater benefit for these
categories of users. Of course, since the remaining users were faster with folders than
with zooming, the interface should be flexible in providing access to both mechanisms
for managing screen space. Here again, the performance of this category of users may
also improve under zooming with additional experience and training.
It was somewhat surprising to see that tool type did not affect subjects?
confidence in their final groups. Feedback from several of the subjects on these
confidence ratings indicated that the task may have been too difficult to reflect any
differences here. In particular, the most difficult portion of the task seemed to come
towards the end when there were a few objects left that did not fit into existing groups.
This often meant that groups had to be reorganized to accommodate the outliers.
However, very few subjects were ultimately able to incorporate all the outliers to their
satisfaction. As a result, subjects seemed to give equally low confidence ratings based on
94
their frustrations at the end of the task. A more revealing measure might have been how
many different organizations they tried before giving up. However, this data would be
difficult to judge for the folder condition since many of the groups were initially
represented by informal spatial clusters in the ?Root? folder.
Lastly, the effect of tool type across labeling strategies on the raw subjective
duration assessment values collected in the study approached significance in a 3x2
ANOVA with F(1,11)=4.818, p < 0.06. However, the goal behind this metric is to take
the difference between the raw subjective duration and the actual task duration to indicate
task difficulty. Tasks that are perceived as being easier to complete have a subjective
duration that is lower than the actual duration and tasks that are perceived as being harder
or interrupted have a higher subjective duration than actual duration. For the data above,
comparing these subjective duration differences did not reveal any significant effects.
One reason for this might be that there really is no difference between the two interface
conditions for this metric. Another possibility is that the study tasks have completion
times that are too long and are therefore beyond the useful range of the metric. The paper
that introduces this metric had average task completion times of around 3 minutes [23].
In contrast, the tasks in this study had completion times of around 20 minutes. These
longer completion times are likely to introduce random noise into the estimates that may
hide any effects of the subjective duration assessment.
2.7.3 Revisited: Scaling Effects and Other Parameters of Task Difficulty
The parameters of difficulty found for the shape task in the previous section
require some adjustment to be applied to text. Perhaps the biggest difference between the
two object types is that the text objects do not have clear attributes. Instead, they have a
95
more amorphous notion oftopics. Text objects also don?t have a discrete number of
values for each attribute as with shapes. Instead, the values for each topic are continuous
weights, from 0 to 1 for example, indicating the extent to which the object is about that
topic. This is illustrated by a sample text object from the previous study that contained
the text ?Pedestrians are not allowed in bike lanes when there are sidewalks.? This object
has at least three potential topics including pedestrians, bikes, and lanes. However, the
weightings of these topics are not equal. ?Pedestrians? is likely to be weighted more
heavily for this object since it is the subject of the sentence.
Topics take longer to discover than the perceptual attributes of shapes. This is
because topics are often represented by only a small percentage of the words in an object.
In some cases, the topic is implicit and topic related words may not appear at all. This
means that the actual number of objects in a task plays a bigger role with text than with
shapes. In the shape task, the data suggested that the number of groups, rather than the
number of objects, was the primary scale factor since each individual object took minimal
time to process. For text objects, the number of groups also affects completion times, but
the actual number of objects is also important since the time to process the individual
objectsismuchlonger.
A second difference between shapes and text is in defining membership criteria
for groups. The shape tasks described above had membership criteria that were well
defined and absolute. This is not necessarily the case for groups of text objects. Topics
for text objects can be related through hierarchical abstraction. As a result, a group may
have membership criteria involving topics that do not explicitly appear in any of the
96
group members. For example, in the tasks in the previous study, all of the facts were on
the general topic of ?driving? though few objects actually contain this word.
Overlap between membership criteria is likely to have a similar effect on the
difficulty of the text task as it had in the shape tasks. Of course, overlap is somewhat
more ambiguous for text objects since topics are weighted and hierarchical. The
weightings on objects mean that objects can have partial overlap on membership criteria.
For example, a text object containing the text ?Cyclists must ride in the same direction as
other traffic? contains some degree of overlap with the ?pedestrians? object mentioned
above since they both implicitly mention bicycles. However, since the two objects have
different weightings on this topic, they do not have complete overlap on this criterion. In
addition, overlap can also have different weightings at different levels of abstraction
since the topics are hierarchical. For instance, the ?pedestrian? object and ?cyclist?
object would have greater overlap under membership criteria such as ?Non-motorized
locomotion.?
Another potential similarity between the text and shape tasks is that the difficulty
increases with the ratio of the total number of topics to the number of group membership
criteria. Just as in the shape condition, as the number of topics in a group of objects
increases, the number of dimension on which the objects can be grouped also increases.
In this case, if the number of topics used to group the objects remains constant, then the
ratio of similar topics to non-similar topics decreases for the group.
These differences and similarities with the shape task suggest the following
revised relationships between task parameters and task difficulty:
1) Difficulty increases with the number of objects to be grouped
97
2) Difficulty increases with the number of groups needed
3) Difficulty increases with the ratio ofthe totalnumber oftopics to the average number
of topics used for membership criteria in each group
4) Difficulty increases with the number of overlapping membership criteria
2.8 Niagara in Use
As part of our future work, we intend to distribute Niagara to a wider audience to
get feedback in using it for actual presentation authoring and other writing tasks. In the
meantime, we have used Niagara ourselves for a number of authoring and bookkeeping
tasks including: organizing 3 presentations, organizing 3 chapters in this dissertation,
creating 6 demos, organizing a movie script, and keeping track of 2 to-do/bug lists.
These initial uses have largely confirmed our hypotheses regarding the utility of
various Niagara features. We have found Niagara?s unconstrained 2D layouts, bumping
behavior, and facile text manipulation to be extremely natural and effective for these
kinds of tasks. Automatic grouping has also met our expectations in providing a low-
effort transition frominformalspatialclusters to more formalstructures. The zoom
controls provided in Niagara have also proved much more valuable than scrolling or
folders as mechanisms for acquiring more space. Certainly, zooming was not needed to
complete all of these tasks since they varied in the amount of information to be
organized. However, zooming seems to become more important as tasks last longer and
more information needs to be organized.
98
Chapter 3:
Niagara Features and Implementation
Niagara was designed as a stand-alone java application that stores its data in a
custom XML file format. The first two versions of Niagara described in Chapter 2 were
built on top of Jazz [10], a Java toolkit for building ZUIs. The final version of Niagara
was built on top of Piccolo [8], a ZUI toolkit designed to replace Jazz. The final Jazz
version of Niagara contained 55 source files representing 183 classes. These classes
contained around 23K lines of code not including comments and 28K lines of code
including comments. The final Piccolo version of Niagara contained 86 source files
representing 328 classes. These classes contained around 13K lines of code not including
comments and 17K lines of code including comments.
3.1 Unique Features in Niagara version 1
3.1.1 Objects and Interactions
There are three object types in Niagara version 1 including text objects, lists, and
subspaces. The text object provides a simple text editor that can wrap at a specified
width or grow to fit all the text. The text object is the foundation of the Niagara
workspace and can contain no other objects. The list object is a collection object that
enforces a vertically-ordered left-aligned layout. Lists can contain text objects, subspaces
or other lists. Subspaces are also collection objects that can contain text objects, lists, or
other subspaces. Subspaces have their own conceptually infinite 2D workspaces in
which objects can be arranged with no constraints on layout. Lists and subspaces both
have instances of a title object that is used to move the object and displays an editable
text label. The top level Niagara workspace has all the properties of a subspace but is
99
special in that it does not have a title and can not be deleted. The three object types are
illustrated in Figure 22.
Figure 22: A screenshot of the three object types in the first version of Niagara. The
objects from left to right are a text object, a list, and a subspace.
These object types are fairly typical of many authoring tools and can be found in
systems such as VKB [100], Tivoli [84], and Storyspace [107]. Niagara?s uniqueness
comes primarily from object interactions. For instance, lists provide interactive previews
as shown in Figure 23. Previous systems have used markers to indicate the point of
insertion in a list, but this technique does not give a good representation of what the list
and the overall workspace will look like once the insertion is completed.
Figure 23: A screenshot of an item being added to a list. The list provides a preview of
what the list will look like when the object is inserted.
100
Subspaces also provide a unique interaction shown in Figure 24. At each level of
nesting, subspaces implement a reduction in scale. This scale reduction is also reflected
during objects movements so that objects over a subspace are immediately reduced in
scale to reflect their appearance when the move is completed. Figure 25 shows the result
of moving an object over nested subspaces.
Figure 24: A screenshot of a list shown in three different levels of nested subspaces. The
scale of objects is gradually reduced as the nesting depth of the subspaces increases.
In addition to reducing scale, the subspaces shown above clip objects that extend
beyond the subspace? rectangular boundaries. Other subspace behaviors that were
considered include reducing the scale of the subspace to fit all contained objects or
growing the subspace to fit all contained objects. Ultimately the current implementation
was chosen because it provides a consistent scale reduction across subspaces while
allowing the author to control the size of the space.
101
In addition to the above features, subspaces behave much like normal operating
system windows. They can be resized, maximized, and restored. In order to indicate
their nesting level when maximized, subspace backgrounds are painted a progressively
darker shade of blue. This is shown in Figure 24. Techniques such as those described by
Furnas et al. [38] could be used to increase the number of levels of nesting possible.
Figure 25: A screenshot of an item being moved over nested subspaces. The item
reduces in size to preview the size it will take when dropped in the space.
Another of Niagara?s distinguishing interactions is its no-occlusion contract. This
specifies the amount that objects are allowed to overlap and was inspired by applications
such as Data Mountain [93] and Flatland [74]. For example, Figure 26 shows a text object
being dropped between two subspaces. Here, the subspaces are moved apart to make
room for the text object. In this specific figure, no overlap is allowed, though this could
also be adjusted to allow the occlusion of a certain number of pixels.
102
Figure 26: A screenshot of a text object being dropped between two text boxes. When
the text object is dropped it ?bumps? other objects out of the way. Prior to being dropped
the text object is in ?flying? mode.
The overlap avoidance described above is also useful for interactively positioning
objects. It allows an author to use any object as a tool to make space or align a set of
objects. An example interaction is shown in Figure 27. Nonetheless, this kind of
interaction can also be disruptive in certain situations. Sometimes the author needs to
move an object across the workspace without disturbing existing arrangements.
Consequently, Niagara also implements a flying mode that allows an author to move
objects without disturbing objects on the workspace. This mode change is portrayed to
the user through via the addition of a shadow, shown in Figure 26. The shadow is meant
to be suggestive of the object flying above the workspace? surface.
Figure 27: The magenta object is used as a tool to make space between the green objects.
103
3.1.2 City Lights
A fundamental challenge in the design of ZUIs or 2D workspaces like Niagara is
to support detailed authoring operations while still providing context or awareness of the
larger containing space. For abstract content, these workspaces often lack good facilities
for portraying objects that are currently not in view. The two traditional techniques used
to address these problems are overview plus detail and fisheye. However, existing
instantiations of these types are often problematic for information work. Overviews do
not necessarily work well for textual content and the overview window intrudes on the
primary workspace. Similarly, most fisheye techniques introduce distortion that can be
harmful to the user?s understanding of spatial location.
City Lights is a simplified fisheye technique for displaying contextual information
in clipped views of 2D information workspaces. The name ?City Lights? was chosen in
keeping with the metaphor of information clusters as cities [26]. Just as the lights from a
physical city are visible at night from greater distances than the city itself, our technique
makes properties of information cities visible when those cities are not in view.
City Lights uses a small, fixed amount of screen space on the frames of windows
for peripheral awareness indicators. The primary advantage to limiting the space
allocated to City Lights is that the majority of the screen can be left undisturbed. This
frees users from having to adjust to a novel presentation of data in the focus region. More
importantly, users do not have to learn new techniques for authoring in a warped or
distorted space. Instead, systems utilizing City Lights can function for the most part as if
the City Lights were not there. In their most rudimentary form they can use a one pixel
line on the window?s border.
104
One way to understand City Lights is as meaningful decorations on the frame of a
window over an infinite canvas. Any visual information objects on the canvas that are not
directly under the window are clipped. As shown in Figure 28, that part of the canvas on
which there are visual information objects is called ?populated space? and that part which
can be seen at a given time is its ?viewed space.? Restated, elements that are in populated
space but not the viewed space are clipped. Panning operations make it possible to move
the viewed space around in the populated space ? analogous to moving the ?window?
over the ?canvas.?
Figure 28: A simplified portrayal of a City Lights technique.
To provide peripheral awareness of the populated space, City Lights provide
information about clipped objects. Such information about a clipped object could include
many things, such as its direction from the boundary, its size, its distance, its type, its
recency or time since last edit, or a summary of its information content.
The initial implementation of City Lights used an orthogonal projection to map
clipped objects? profiles onto the window borders. Figure 28 shows an abstracted
Populated space
Viewed space
105
depiction of the technique with blue boxes representing visual information objects that
are inside the viewed space and red boxes indicating clipped objects in populated space.
The city lights are shown as green colorings on the outer bounding box of the viewed
space. An orthogonal projection from the clipped objects onto the frame bounding
populated space delineates the edges of the city lights. Because this orthogonal
projection ignores objects in the corners, decorations were also added to indicate when
objects were in the corner regions.
The need for peripheral awareness also often exists simultaneously at multiple
levels in a hierarchy. Nested hierarchy levels can themselves introduce problems in the
management of focus and context. The user needs to maintain regions of focus at several
levels simultaneously. Our approach enables clipping of the populated spaces around
each viewed space at any level, and manages the competition for screen space for each
window. Figure 29 shows City Lights on Niagara subspaces at multiple levels.
Figure 29: Nested Niagara subspaces with City Lights at multiple levels.
City Lights can also be interactive. The current implementation allows users to
click on a City Light visualization to navigate to the clipped object. Future
106
implementations can support more sophisticated interactions such as the fisheye behavior
implemented by the Macintosh OS X taskbar.
The left screenshot in Figure 30 shows the initial Niagara City Lights
implementation. These City Lights try to convey information about the size of off-screen
objects. However, as the number of objects in the populated space increases, the window
borders quickly become cluttered by this kind of representation. An alternative space-
conserving approach, shown on the right in Figure 30, portrays only the object centers in
the City Lights.
Figure 30: The first in a sequence of screenshots showing Niagara workspaces with 13
off-screen objects. To show these off-screen objects, overview windows are displayed on
top of the primary workspace windows. The overview windows show the view rectangle
in black and the unseen objects outside this view. These two screenshots show two
versions of City Lights along the larger windows? border using an orthogonal projection.
The left screenshot shows a line projection of the object bounds and the right screenshot
shows a point projection of the object centers.
107
The orthogonal projections used in the original City Lights implementations are
roughly based on the physical metaphor of light. This simple metaphor is particular
intuitive since most windows are Manhattan rectangles and panning takes place in a
Cartesian space. However, this projection requires special considerations for objects in
the corners. In contrast, a radial projection can be more consistent in certain situations by
eliminating the special case for the corners. Figure 31 compares these two projections
side-by-side.
Figure 31: Two versions of City Lights are shown along the larger windows? border. The
left screenshot shows an orthogonal projection of object center points and the right
screenshot shows a radial projection of object center points.
A variant of City Lights was also developed that uses ellipses to represent off-
screen objects. This is shown next to a radial point projection in Figure 32. Baudisch et
al. named these circular projections ?halos? and demonstrated their effectiveness for
different kinds of peripheral awareness tasks [4]. This technique seems to be most suited
for applications with a small number of relevant off-screen objects where distance
judgments are important.
108
Figure 32: Two versions of City Lights are shown along the larger windows? border. The
left screenshot shows a radial point projection of object centers and the right screenshot
shows ?halos? centered on the objects? centers.
Just as physical light attenuates with distance, City Lights can represent distance by
manipulating contrast or color. In our current implementations, near objects receive a
higher contrast color and far objects receive a lower contrast color. These color changes
are shown in the screenshot on the left of Figure 33. City lights for objects that are nearer
than a near boundary are colored dark green. City lights for objects that are farther away
are colored light green. Indicators for near objects occlude indicators for far objects. This
color difference is more apparent in the screenshot on the right of Figure 33, which
assigns blue to near objects and red to far objects. In addition, City Lights can also use
color gradients to indicate a wider range of distances.
More City Lights details, variations, and usage scenarios are described in our CHI
short paper [121].
109
Figure 33: Two versions of City Lights are shown along the larger windows' border that
use a radial projection of object center points. The left screenshot uses a binary color
system with near objects in darker green and far objects in lighter green. The right
screenshot makes the difference clearer by using blue for near objects and red for far
objects.
3.2 Unique Features in Niagara version 2
3.2.1 Spatial Overview Features
One of the primary changes to the Niagara version 2 prototype was the addition of
a spatial overview. Although spatial overviews are a frequently used interface technique,
there seem to be few design guidelines for their use with abstract data. As a result,
several designs were tried in our initial implementation. Figure 34 shows three of these
designs that vary from the most accurate on the left to the most abstract on the right. The
rightmost design is similar to the overview available in the VKB system [100].
110
Figure 34: This figure shows three versions of the spatial overview. The leftmost version
shows a standard geometrically reduced representation of the entire space. The center
version shows an overview with semantic zooming on collection titles. The rightmost
version shows an abstracted version of the objects in the workspace.
By default, Niagara used the center design in Figure 34. This design portrayed
the overall geometric properties of the space while still preserving some of the high level
text content. This design also facilitates the common operation of moving an item from
the current workspace to a known collection. To support this interaction, the overview
implemented drag and drop functionality. This meant that a user could drag an object
from the main workspace and drop it into an off-screen subspace via the overview.
However, we also noted that this functionality only worked for a single level. As result,
we implemented a drill-down pop-up interface shown in Figure 35. When the user
paused the mouse over a subspace in the overview during a drag and drop interaction, the
system would pop-up a window that displayed an overview of the target subspace. This
worked recursively so the author could drill down to access arbitrarily nested subspaces.
111
Figure 35: A screenshot of an interactive spatial overview. When the user pauses while
dragging an itemonto a subspace in the overview, the systempops up a window to show
an overview of the current subspace.
The author does not always want to decide an object?s exact position when
moving the object into a subspace as in Figure 35. Instead, the author sometimes just
wants to move the object into a subspace for later processing. In these cases, Niagara
tries to automatically position the object as close to the center of the subspace as possible
without overlapping other objects. This strategy leads to a good spatial packing of
objects, reducing the size of the workspace that the author needs to manage later. The
algorithm Niagara uses to find empty space and position objects is based on the work of
Bell and Feiner [11].
112
Another feature in Niagara provided an overview of a subspace?s entire contents
when the subspace was selected. This is illustrated in Figure 36. This allowed an author
to get a preview of subspace without having to maximize or manually resize the space.
Figure 36: The overview switches to show the currently selected workspace.
An additional non-standard feature in the Niagara overview was the ability to
move to non-populated space. Traditional spatial overviews restrict the view rectangle?s
movement based on the current workspace size. Niagara allows the user to move the
view rectangle to arbitrary points in space, including those outside bounds of the
113
currently populated workspace. This feature may be important for authoring workspaces
like Niagara?s since the author willoften need a wayto get more roomto work.
Niagara?s spatial overview was also adapted to work with the Focus Plus Context
screen [3]. The Focus Plus Context screen uses a standard LCD flat panel for the high
resolution focus and a video projector for the low resolution periphery. Figure 37 shows
an example of what this display looked like in use.
Figure 37: A screenshot of a prototype overview developed for use with the Focus Plus
Context display.
114
3.3 Unique Features in Niagara version 3
Three of the most important features added in the third round of Niagara design
were automatic text reduction, automatic grouping, and full text popups. These features
were described in Section 2.5. Several other interesting Niagara version 3 features are
described below.
3.3.1 Similarity Matching
One additional feature in the third version of Niagara was similarity matching.
This feature was intended to address a recurring behavior observed in Niagara where
authors would group facts into collections based on the appearance of similar important
words in each of the facts. In Figure 38, the similarity indicators can be seen as red
overlay text (e.g. ?illegal? and ?bac?) on the text objects in the workspace. The current
fact for which the similarity indicators are being computed is shown with a dark
background in the upper right corner of the figure. These similarity indicators are
calculated using different linguistic techniques, such as TFIDF, to identify important
words in the entire Niagara workspace that also occur in the current fact.
Although these similarity indicators will sometimes be incorrect, having a high
threshold for similarity will reduce the chances of inaccurate results. Interestingly, an
analogous type of similarity indicator was implemented in the Data Mountain to help
subjects organize web favorites [22]. This study suggested that the similarity indicators
encouraged a larger number of category creations, longer organization times, and shorter
retrieval times.
115
Figure 38: A screenshot of similarity matching cues shown in red. The user can request
these cues when moving an item in the workspace or dragging in a new item from one of
Niagara?s information sources.
3.3.2 Overview and Group Representations
Based on our experiences in Niagara version 2, the spatial overview was replaced
with a structural overview (also called a tree or outline view) shown in Figure 39. Since
the overview was largely intended to provide access and readability of group labels, the
structural overview offered a more useful layout for text labels than a spatial overview.
In particular, the structural view had the advantage that it could show an overview of
multiple levels for nested hierarchical collections.
116
Subspaces in previous versions of Niagara were replaced by folders in the
structural overview. Folders act like subspaces in Niagara version 2 in that they each
have their own infinite 2D workspace. They behave in essentially the same way as
folders used in the file browsers on many operating systems. Figure 39 shows a Niagara
workspace with a single folder labeled ?Root.?
Figure 39: In the structural/folder view, the system represents automatically created
groups with a label and an iconic representation of the group?s spatial layout.
There was also an interesting crossover between the structural overview and
automatic grouping. The automatic groups were represented in the overview with an icon
and a text label if one was given. The group icon portrayed a miniature version of the
group?s spatial layout using the group?s automatically assigned color. Several of these
icons are portrayed in the overview in Figure 39. The author could double click on these
117
icons to navigate to the group in the main view or drag the icon onto other groups or
folders for quick reorganization.
Since the structural overview represented a linearization of a 2D workspace there
were also choices in how groups were sorted. Some potential sorting criteria included
spatial position from top-left to bottom-right, alphabetical order of group labels, and
creation times. Folders could also be separated from or interleaved with automatic group
labels. By default, Niagara separated folders and group labels at each level then sorted
first alphabetically then by creation time.
In addition, several alternative automatic grouping representations were also
considered. The current implementation shown in Figure 39 added a persistent title and
rectangular border. However, we also considered variations of an implementation, shown
in Figure 40, that only changed the color of grouped objects. Moving the mouse over the
group would then invoke a group handle that allowed the author to move the entire group.
Figure 40: One implemented version of automatic grouping that changed object colors
but did not display a title bar or rectangular outline.
118
3.3.3 Zooming
As described above, an author could move objects into subspaces for limited
control over scale in previous versions of Niagara. However, prior to version 3, Niagara
disabled manual zooming of the entire workspace. This restriction was partly due to the
lack of good semantic zooming for text objects and also partly to avoid the complexities
of independent zooming in multiple nested subspaces.
Niagara version 3 eliminated the previous notion of subspaces while introducing
automatic text reduction. These changes facilitated manual zooming of the entire
workspace by avoiding the complexities of multiple independent zoom levels and by
providing more meaningful views of zoomed out text objects.
A number of different zooming interactions were also considered in Niagara. To
avoid many of the navigation problems described in previous ZUI applications, the
Niagara implementations completely eliminated unconstrained zooming. Instead, all
variants of zoom interactions implemented in Niagara limited zooming to both a
minimum and maximum level. It should also be noted that all objects were located at the
same baseline scale to further constrain the space and reduce the chances of misplacing
objects or getting lost.
Niagara?s first zooming interactions were based on those found in PhotoMesa [6]
and Nested User Interface Components [86]. This type of interaction computes a discrete
number of zoom levels based on the workspace contents that the user can access through
clicking. For example, Niagara initially allowed the user to zoom in on an object by left
clicking and then progressively zoom out by right clicking.
119
This approach provided a highly intuitive interface for zooming but did not offer
enough control for organizing text. In particular, we found that different users often
wanted to zoomout in different amounts to get more roomto work. However, users were
also often balancing the amount of text they could see in each text object at a particular
zoom level, which was highly dependent on the size of the window and the layout of the
objects in the workspace. Of course, more discrete zoom levels were added, but this
tended to make the technique more inconvenient and less useful.
The second interaction implemented in Niagara tied zooming to the mouse
scrollwheel (or middle mouse button). Rather than discrete zoom levels, the scrollwheel
allowed the user to rapidly zoom in and out through a series of small increments. While
this approach provides more control than the previous one, it raises the issue of defining
the point around which zooming would occur. Zooming in around the mouse location is
fairly intuitive since you basically just point where you want to go. However, zooming
out around the mouse location is fairly unintuitive since the object under the mouse does
not move to the center of the window. One possible way to make zooming more intuitive
is to modify zooming out to zoom around the center of the view. However, this loses the
zooming interaction?s idempotent quality, which can also be confusing. The solution
currently adopted in Niagara instead is to zoom in and out around the center of the
current view. However, future versions of Niagara should probably provide the user with
options to controlthese aspects ofthe zoomcontrol.
Several redundant zoom controls were also added in Niagara to further simplify
zooming. Right clicking on the workspace background would zoom out to see the entire
workspace. Right clicking on an object brings up a popup menu with the option to zoom
120
in on the object or zoom out to see the entire workspace. Several toolbar buttons also
provided the user with the ability to zoom in and out in increments and to zoom out to see
the entire workspace.
3.4 Implementation Details
3.4.1 Bumping
One of Niagara?s relatively unique features was its bumping or overlap-avoidance
policy. In bumping interactions, the user moves an object so that it overlaps another
object or objects and the system must reposition the bumped objects to resolve the
overlap. Of course, there are a number of possible ways to resolve the overlap, but the
goal in Niagara is to give the interface a physical quality. This means that overlap
avoidance schemes like those described by Bell et al. [11] are not suitable since they do
not enforce continuous object movements. Their algorithm instead allows bumped
objects to jump to the nearest open space to avoid the problem of bumped objects
bumping other objects.
Instead, the algorithm needed for the more physical behavior must move objects
continuously and iterate from the original bumped objects to the objects bumped by these
objects, etc. This helps preserve the illusion of solidity suggested by Change et al. [18].
Our algorithm keeps a queue of bumped objects starting with the initial moved object.
Then, while the queue is not empty, the head of the queue is removed and tested for
collisions with other objects. If necessary for efficiency, this collision testing can make
use of techniques such as interval trees [21, 96]. When collisions are found, the bumped
objects are moved to resolve the collision and added to the tail of the queue. The
algorithm then loops. As long as objects are convex and collisions move the objects in a
121
consistent horizontal and vertical direction, this process will terminate since it is
monotonic. It should also be noted that minor unexpected behaviors can result from
discretely sampled mouse motions. This is illustrated in Figure 41 for a diagonal
movement. Although it would not be difficult to add, the bumping algorithm in Niagara
did not interpolate mouse movements.
Bumping in Niagara also interacted with semantic zooming in an unexpected way.
Niagara implemented semantic zooming to enforce a minimum font height for group title
bars and one line text objects when viewed zoomed out. Since objects had larger relative
sizes when viewed zoomed out, bumping often resulted in large spaces between objects
that were only visible when the workspace was viewed zoomed in. For example in
Figure 39, if the group labeled ?Delivery (7)? were to bump based on it?s current title
height, then there would be a space between the ?Delivery (7)? title bar and the
?Authoring (8)? group when viewed zoomed in. To address this problem, Niagara
performs bumping based on an object?s native size rather than its current rendering size.
Figure 41: Discrete movements must be interpolated to obtain the ideal bumping
behavior. In the movement shown above, the red object does not bump the green object
unless the movements are interpolated.
122
3.4.2 City Lights
An important implementation concern with City Lights is scale. Figure 28
demonstrates the use of orthographic projection for displaying object distance and size in
the first implementation of City Lights. In this approach, objects in the ?corners? of
populated space receive a disproportionately smaller space for conveying their
information. The corners are akin to ?blind spots? in driving a car ? that zone where the
use of mirrors gives little or no coverage for viewing other automobiles.
How does the number of objects in the blind spot change with scale? As shown in
Figure 42, as the ratio of the size of populated space to viewed space increases, the
percentage of objects in the blind spot increases. For square windows where the bounding
side of populated space is three times the length of the viewed space, already half of the
objects in the populated space are in the blind spots. The use of a radial rather than
orthogonal projection addresses this issue by eliminating the blind spot. Consequently,
the radial projection performs more consistently when the populated space is much larger
than the viewed space.
123
Figure 42: The red rectangle in both drawings represents the viewed space and the outer
rectangle represents the populated space. The shaded areas represent the set of objects
projected onto the window borders by an orthogonal projection. As the ratio of populated
space to viewed space increases the percentage of the objects in the orthogonal
projection?s blind spots also increases, as can be seen by the growing corner rectangles
above.
3.4.3 Automatic Text Reduction
Automatic text reduction, described in Section 2.5, makes use of a text reducing
function to generate different length texts based on the given size requirement. Niagara?s
automatic text reduction focuses on condensing small collections of text snippets or
paragraphs rather than larger texts. This violates the assumptions of many traditional
linguistic techniques [67]. In addition, Niagara?s text reduction has a different goal than
many linguistic techniques. Since users are familiar with the textual content, the purpose
of text reduction is primarily to allow users to recognize text elements they created or
x
3x
x
5x
124
selected earlier. Therefore the reduced text need not carry full meaning or be
comprehensible and strictly correct.
We have informally experimented with combinations of several simple text
reducing functions including rankings based on: universal word frequencies, word
length, word position, syntactic role, and TFIDF. A common trend we found in several
of these rankings is that they eliminate short words. This seems to be a result of these
words? frequent occurrence, their appearance in stop word lists, and their use in limited
syntactic roles. Because of these similar results, we found that many of these reduction
techniques work sufficiently well for our purposes. Further user testing is needed to
determine whether Niagara requires more complex language models for optimal text
reduction.
The current implementation also reduces text length by shortening individual
words. The techniques used to shorten words include stemming, removing vowels, and
truncation. In the current implementation, these techniques are only employed when
complete, unmodified words will not fit in the remaining available space. Of course,
future implementations could use more sophisticated models for combining word
elimination and intra-word reductions.
The current text reduction implementation follows the general pseudocode
procedure below:
Initialize PrevWordList to contain all words in full text
For each level of reduction
Initialize LevelWordList as an empty list
Compute available space at current level
Rank words in PrevWordList using a combination of TFIDF and global
word frequencies
For each word in PrevWordList
125
If space available, respecting original text order, and
LevelWordList does not already contain word
Add word to LevelWordList
If word added
Remove word from PrevWordList
For each word in PrevWordList
If space available, respecting original text order, and
LevelWordList does not already contain word
Add stemmed word to LevelWordList
If stemmed word added
Remove word from PrevWordList
For each word in PrevWordList
If space available respecting original text order, and
LevelWordList does not already contain word
Add word with vowels removed to LevelWordList
If vowel-removed word added
Remove word from PrevWordList
If LevelWordList is emtpy
Truncate top-ranked word to fit available space
Add word to LevelWordList
Set PrevWordList equal to LevelWordList
Automatic text reduction introduces a tradeoff between font size and content
length. As the font size is reduced, more content can be shown in an object. However,
reading the text at the reduced size becomes correspondingly more difficult. Niagara
chooses a balance between these two, shown in Figure 43, that alternates between
reducing font size and reducing text length. A subtle problem that arises in applying this
alternating strategy across objects is that it can lead to different font sizes for different
objects. This results in a patchwork display of font sizes that is aesthetically unappealing
and even potentially distracting. Niagara deals with this problem by coordinating the two
kinds of reduction across objects at predefined zoom levels. For example, Niagara
implements content reductions on all objects when the scale is a multiple of 0.84 (or
0.5
0.25
). At these points of content reduction, the font size is reset to maximum and then
gradually decreases with scale until the next content reduction point. Other applications
126
using automatic text reduction will likely need a similar coordination, though they can
choose different transitions through the graph in Figure 43 based on the requirements of
the task.
Figure 43: A graph of text reduction in terms of font size and text length. The black dots
indicate the states represented in Figure 6. The curves indicate objects of the same size.
It should be noted that the curves in Figure 43 are only approximate. In actuality,
text cannot be smoothly reduced but instead must be reduced in discrete increments due
to letter, word, and line boundaries. Discrete line boundaries are particularly limiting
since each line can contain a potentially unlimited amount of text. As a result, each line
that is eliminated in automatic text reduction can lead to a significant reduction in the
quality of the text. The number of lines that can be shown in a text object is highly
dependent on the font size and aspect ratio of the object. For example, Figure 44
demonstrates how two text boxes with equivalent area, line spacing, insets, and font sizes
can show different numbers of lines and hold different amounts of text.
The size of the text object itself also has a strong effect on automatic text
reduction. Just as with non-text objects in zoomable space, smaller text objects become
less recognizable than bigger text objects the further you zoom out. However, this is
text length
font
s
i
z
e
127
intrinsic in the nature of zoomable space so it is not necessarily something that should be
fixed. That being said, the current implementation does try to delay this effect in
Niagara. In particular, Niagara implements a one line minimum height on all text
objects. Figure 39 shows a zoomed out Niagara workspace with a number of single line
text objects that have grown beyond their original size to enforce the minimum height.
The figure also illustrates the primary tradeoff to this approach, namely that objects begin
to overlap the more you zoomout. However, without this approach, font size would be
greatly reduced leaving essentially none of the text readable.
Figure 44: Two text boxes with equal area and equal font size. The right text box holds
less text because its height is between multiples of the font height.
In an interactive application like Niagara, speed is a primary concern in
implementing automatic text reduction. Niagara often has many text objects, where each
text object has many levels of reduction. This makes it difficult to compute text
reduction on the fly for each object during interactive zooming. Instead, it is often
necessary to pre-compute and cache all necessary text reduction levels as each text object
is created and edited. Fortunately, the need for caching text reduction levels integrates
nicely with the need to coordinate font sizes across objects. In order to coordinate font
size in Niagara, the font is reduced until the scale reaches a power of a given ratio r,then
the content is reduced and the font returns to its original size. The text lengths of these
cached levels form a geometric series with a ratio of r, so we can predict that the total
The quick brown
fox jumps over
the lazy dog.
The quick brown fox jumps over
128
memory required to cache these levels will be less than 1/(1-r) times the memory of the
original text. In Niagara, r issetto0.84sothattextobjectswitha12pointfontwillnot
fall below a 10 point font. This value of r means the memory is limited to 6.25 times the
original text. In practice, the actual memory requirement is much lower since we
implement a minimum text length rather than continuing the geometric series to infinity.
In addition, other memory saving techniques can be used such as using text references
rather than text copies for the cached levels to limit the memory requirements of caching
even further.
129
Chapter 4:
Presentation Delivery and CounterPoint
4.1 Related Work
The work in this chapter builds heavily on the slide show metaphor. As a result, it
also further develops many of the ideas found in the numerous software slide presentation
tools including Corel Presentations [91], Freelance Graphics [34], Hancom Presenter [47],
Harvard Graphics [48], Impress [57], Keynote [63], KPresenter [64], Persuasion [87], and
PowerPoint [89]. These commercial tools primarily provide a software interface for
mimicking physical presentation media such as 35mm slides or overhead transparencies.
CounterPoint, a ZUI presentation tool described in this chapter, extends the techniques
and metaphors employed in these tools.
4.1.1 Navigating Presentations
Because of their demanding conditions, slide show presentations have long made
use of extremely simple navigation controls. The most basic slide show presentations
provide only two navigation controls, namely moving forward one slide and backward
one slide. These simple techniques work fine until the presenter wants to jump out of
their linear sequence. At that point, more advanced controls are needed.
One extension to this purely linear traversal through a collection of information
was Zellweger's Scripted Documents [119, 120]. Scripted Documents allowed the author
to define timed traversals through a collection of documents with specifiable actions
performed at each stop in the traversal. Conditional and customizable paths were also
described that allowed paths to be modified to fit the users current needs. The
130
?Audio-visual presentation? application of scripts described in Zellweger?s earlier
work closely resembles the use of scripted paths described in this chapter.
Trigg's Guided Tours and Tabletops also described a more dynamic version of
linear paths [112]. The system described in the paper provided tools for creating a
collection of "tabletops," each of which contained a spatial arrangement of notes or
documents. An author could then define arbitrary paths through these tabletops with any
number of available branches at each point in the path. Here again, the scripted paths
described in this chapter resemble Trigg's paths in that they can combine both scripted
and dynamic components. Both also follow the traversal defined by the scripted path
unless special actions are taken. However, CounterPoint paths differ from these tours in
that they traverse data in a single continuous space instead of sets of disjoint spatial
arrangements.
World Wide Web-style hyperlinking also provides an interactive extension to
traditional linear paths. These hyperlinks can be found as a navigation aid in several
commercial presentation tools including PowerPoint [89]. One practical application of
hyperlinked paths in the presentation setting was Moore?s use in teaching an
undergraduate Computer Science course [73]. Moore found that the use ofthis kind of
traditional hypertext facilitated hierarchical organization and also allowed for
interconnection of related material, both of which potentially improved navigation.
However, hyperlinks have the drawback that they require an author to create them prior
to giving the presentation. As a result, the presenter must anticipate all potential branches
that might be required during a presentation.
131
Another tool suggested for improving navigation in slide presentations is Hyper
Mochi Sheet [111]. Hyper Mochi Sheet employs a multi-focus distortion-oriented view to
display a hypertext network. During a presentation, the system automatically resizes
nodes in the network based on the presenter?s current focus. While the multi-focus views
allow it to show focus and context, its dynamic nature makes it harder for the presenter to
predict. Thus it is often less desirable for presentations where layouts and object sizes are
parameters of primary concern.
Several recent techniques address presentation navigation through extensions to
the existing slide show metaphor. The Palette system [75] allows presenters to navigate a
standard slide presentation using a barcode reader with paper copies of slides. The
barcode on the paper slide acted as a reference to the PowerPoint file and slide number of
its corresponding virtual slide. This allows presenters to dynamically combine slides
from multiple PowerPoint files into a single presentation and to improvise slide orderings
at presentation time. The paper slides also allowed presenters to search for a particular
slide by flipping through a physical stack or spread the slides out on the desk or podium.
Of course, this approach also has drawbacks. In particular, it requires users to manage
both the physical and virtual representations of their slides.
In contrast, Dieberger et al. describe a pure software technique for presentation
navigation [27]. Their technique uses a visualization along the left edge of the current
slide to facilitate movement within the presentation. This visualization also displays
information about which slides have been visited and various other timing statistics.
132
4.1.2 Navigating in ZUIs
Simplified interaction techniques are more important for slide presentation tools
than traditional desktop applications since strict time limitations, concerns about
appearance, and the potential for audience confusion often increase the cost of errors. In
this regard, it would seem that ZUIs are not a natural fit for the presentation setting since
they are notoriously difficult to control. Navigation and spatial awareness have been
noted as particular problems in ZUIs [36, 61]. Nevertheless, recent advances in ZUI
navigation have helped alleviate these problems in many cases.
Navigation in 2D workspaces has traditionally been achieved through panning
and zooming or dedicated widgets such as scrollbars or overviews. However, these
techniques do not take into consideration the specific challenge of ZUIs that information
can reside at multiple zoom levels. One particular problem using traditional techniques
in large multiscale spaces is disorientation. Furnas et al. propose the space scale editor as
a technique to address disorientation in multiscale ZUI spaces [37]. The Space Scale
Editor is an additional view that augments a traditional 2D workspace with a
visualization of multiple zoom levels. This view offers awareness of object positions at
different zoom levels while displaying a visual representation of the primary view?s
position in the multiscale space. An alternate technique proposed by Jul et al. addresses
disorientation through visual cues integrated into the primary 2D workspace [61]. These
visual cues enhance the standard workspace view by indicating interesting regions
containing objects too small to be seen at the current zoom level. CounterPoint tries to
avoid the problems addressed by these techniques simply by limiting the range of scales
at which objects can reside and the amount the user can zoom in or out.
133
A second problem with traditional ZUI navigation techniques is that manual
panning and zooming often require a lot of attention and coordination. One technique
used to reduce the attention and coordination needed in large documents is speed
dependent automatic zooming [56]. This approach automatically combines zooming and
rate-based scrolling to maintain a constant visual information flow as the scrolling
velocity varies. An even more constrained approach is used in PhotoMesa to simplify
navigation in a 2D layout of photos [6]. Here the user primarily navigates through
animated step-wise zooming; left clicking to zoom in and right clicking to zoom out. A
similar type of simplified navigation was also employed in Nested User Interface
Components [86]. A version of this kind of step-wise zooming has been implemented for
navigating in CounterPoint presentations.
4.1.3 ZUI Narratives
Presentations tell a kind of story. As such, the work in this chapter builds on
previous work in using ZUIs for narratives. One early exploration in using ZUIs for
narrative was a work titled GrayMatters [114]. Gray Matters laid out pictures and text in
a Pad++ workspace that a user could then navigate through panning, zooming, and
animated hyperlinks. This work was primarily artistic in nature, hence no evaluations
were performed.
Using ZUIs for narrative has also been explored in the context of a children?s
story-authoring toolcalled KidPad [30]. Kidpad provides simplified tools to allow
children to create primarily vector based drawings that can be linked together in a
zoomable workspace. These links then support the child in dynamically navigating the
workspace to tell different stories based on the illustrations. Boltman et al. looked at
134
these kinds of ZUI stories in a study of 72 children [13]. The study involved three
versions of a wordless children?s book included a paper-based version, a traditional
computer-based hyperlinked version, and a computer-based ZUI version with animated
panning, zooming, and fading transitions between images. The study used a between-
subjects design and found that the ZUI stories provided significant improvements over
the other two conditions in different elements of elaboration and recall.
ZUIs were also studied for readers of nonfiction. Paez et al. looked at using ZUIs
for a document reading task [80]. They performed a between-subjects experiment with 36
participants comparing a document laid out in a ZUI workspace to a hyperlinked
document in a traditional web browser. The task in the study required the user to find the
answer to 5 questions within the document using one of the two interface types. They
found no significant differences between interface types in completion times,
comprehension measures, disorientation measures, or subjective satisfaction. Subjects in
the study did report that the ZUI provided good skimming and overview capabilities and
was easy to learn. This study is particularly interesting for the current research since the
interfaces are fairly similar to those discussed in this chapter. In particular, the
hyperlinked document is similar to a traditional slide show presentation and the ZUI
document is similar to a ZUI presentation. Of course, a slide presentation is different on
several levels from document reading task, yet we expect that ZUI presentations will
have many of the same properties suggested in this work.
4.2 CounterPoint
Although ZUIs are a relatively new, a number of ZUI presentations have been
created prior to this work. These presentations were originally created and delivered
135
using Paddraw [9], a general purpose ZUI authoring tool built on top of Pad++. More
recently, these presentations were created using Hinote [10], another general purpose
authoring tool built on top of Jazz. Creating presentations with these tools can be likened
to creating slides for a traditional slide show with a general purpose drawing program.
The tool provides tremendous freedom and all the necessary functionality, but it does not
offer any specific shortcuts for common presentation tasks. This trade-off in specificity
versus generality has actually been explored for slide authoring with general drawing
tools versus specific slide authoring software [59]. The authors identified a number of
tradeoffs between the two techniques but suggested that the presentation-specific tools
are likely to become more desirable as the number of authored presentations increases.
Many of the tradeoffs described in this work are likely to apply to ZUI presentation
authoring tools as well.
This work comes out of many years of ongoing research into ZUIs and their
actual use for presentations. As such, it builds on the experiences described above in
authoring and delivering presentations with existing ZUI authoring tools. Based on these
experiences, this chapter introduces a presentation-specific ZUI authoring tool called
CounterPoint that is primarily intended to address the complexities involved in authoring
ZUI presentations. This chapter also provides evidence for the various benefits and
limitations in using ZUIs for presentation. Additional information can also be found in
our published work on CounterPoint [42].
4.2.1 Early Mockups
Prior to actually implementing CounterPoint, David Feldman
(www.interfacethis.com) created a specification and several mockups describing the
136
expected functionality in CounterPoint. The mockups portray a hierarchical editing mode
for authoring textual presentation content (Figure 45) and a path editing mode for
authoring traversals through the content hierarchy (Figure 46).
Figure 45: A mockup of a hierarchical editor for authoring textual presentation content.
As previously mentioned, text authoring tasks often organize information using a
hierarchical structure. These mockups are therefore a natural starting point for
CounterPoint since they are designed around hierarchical editing tools. They were also a
natural extension of previous work in ZUIs, such as the Pad++ directory browser [9], that
mapped levels of a hierarchy to zoom levels in the zoomable workspace.
137
Figure 46: A mockup of a path editor for authoring paths through the content hierarchy.
4.2.2 A Functional Prototype
The hierarchies shown in the previous mockups had an intuitive appeal for
presentation authoring since all ideas were given equal representation. In particular, all
ideas had an icon and a label. This allowed them to be displayed using existing tree
representations and interactions. However, in practice presentations are often not purely
hierarchical at the level of individual ideas or bullets. For example, in existing
presentation software, presenters often create an introductory slide containing overview
information for a topic and then additional slides containing the details. In this case, the
overview information on the introductory slide is not a subcomponent of the topic in the
same sense as the detail slides.
138
This suggests that a hierarchical decomposition at the level of individual bullets
and ideas is the wrong granularity for presentation slide authoring. Instead, presenters
tend to think about their presentations in terms of view-sized chunks or slides. This
allows them to maximize the value of individual views and limit the number of
transitions needed between views. Ultimately, this strategy minimizes the amount of
attention needed to control the presentation tool as well as any distractions caused by the
superfluous transitions.
An additional practical benefit of using slides or discrete views as the unit of
information in ZUI presentations is that authoring tools can leverage users' existing
presentation authoring skills. Because the notion of slides remains essentially the same,
ZUI presentation authoring environments can provide similar objects and operations as in
traditional slide authoring tools.
Based on these early lessons, a CounterPoint prototype was implemented, shown
in Figure 47, that provides a hierarchical decomposition using slides as the level of
granularity. The prototype also integrates with PowerPoint, an existing slide authoring
tool. This allows the tool to take full advantage of presenters? existing slide authoring
experience since many presenters are already using PowerPoint for presentations. In
addition, it also allows presenters to reuse existing slides that have been authored and
saved in PowerPoint?s specific format.
Much of the benefit of CounterPoint?s ZUI interface for presentations comes from
having structure on top of the slides. As a result, CounterPoint requires additional effort
beyond individual slide authoring to combine these slides into a single zoomable
structure. While creating arbitrary spatial layouts can be difficult in a zooming
139
environment, this process can be simplified by separating out the conceptual structure
from the spatial structure. This allows the conceptual structure, which is often well
defined, to automatically determine the spatial structure, which can be more subjectively
or aesthetically defined. Of course, the automatically defined spatial structure can also
act as a starting point, allowing the presenter to tweak the spatial layout to emphasize
certain ideas, improve memorability, or customize the aesthetics.
Figure 47: A screenshot of the CounterPoint interface in the hierarchical layout editing
mode for a presentation on Automatic Text Reduction [43].
As the mockup in Figure 46 suggests, CounterPoint also supports multiple paths
through a single presentation hierarchy. As a result, an additional authoring challenge is
creating and editing these paths. The automated approach to defining these paths is to
140
use the presentation's structure. In this case, the spatial layout can be used to define a
default ordering of slides, such as a depth first search. In addition to spatial order, the
conceptual structure can be used to inform the ordering based on group membership and
to define reasonable overviews of groups within the path. As in authoring layouts, this
default path can also serve as a starting point for more customized paths.
In addition to helping layout the space and define a path, the hierarchical structure
defined in CounterPoint can assist the presenter in navigating the presentation when
answering questions or responding to unexpected time constraints. For example, in
jumping from a slide in the conclusion to a slide on an earlier topic, the presenter can
simply move up the conceptual hierarchy to find the high level representative for the
topic then drill down in that topic to find the appropriate detail slide. Restated, the
conceptual hierarchy facilitates a search with logarithmic complexity. Because of these
efficient navigations, scripted paths may become less important in CounterPoint as
presenters begin to rely on the customized navigation controls available in presentation
mode. Still, some presenters are likely to rely on scripted paths because of the reduced
chances of error.
More details on the CounterPoint features and implementation can be found in
Chapter 5, titled ?CounterPoint Implementation and Features.?
4.3 The Audience? Perspective
Previous research in HCI, hypertext, cognitive psychology, and educational
psychology suggests mechanisms by which CounterPoint presentations may offer
cognitive advantages to the audience over traditional slide show presentations. In many
ofthe studies below, the conditions and tasks used differ fromthe presentation setting.
141
However, there are also enough similarities to justify some level of comparison.
Moreover, informal experience with CounterPoint presentations offered some evidence
that the audience benefits from these presentations.
Of course, individual differences undoubtedly influence the cognitive utility of
ZUI presentations for the audience. Indeed, animations [50] and spatial arrangements [1],
two elements of ZUIs, have been shown to support greater recall in users with low spatial
abilities than in users with high spatial abilities. Fortunately, these studies give some
further evidence that animations and spatial arrangements do not negatively impact recall
in users with high spatial abilities. Likewise, research into concept maps (see Figure 48)
suggests that subjects with low verbal ability may benefit more from a spatial display
than from a textual display [83]. Here again the type of display did not adversely affect
the high verbal ability students. With the caveats in mind, listed below are some potential
cognitive benefits of ZUI presentations and supporting evidence from related work.
Following a discussion of the potential benefits, a preliminary user study is
presented to explore the extent to which these benefits appear for an actual slide
presentation.
4.3.1 Potential Cognitive Benefits
4.3.1.1 Dual Encoding in Memory
Presentation tools are typically accompanied by a presenter's oral discourse.
Hence, the audience receives simultaneous visual input from the presentation tool and
verbal input from the presenter. Therefore, an interesting question is whether humans
learn differently from these two streams of data. Cognitive hypotheses suggest that
human memorydoes encode spatialinformation distinctlyfromverbalinformation (for
142
example, see [2, 81] and discussions in [1, 95]). As a result, a presentation tool may
exercise a larger portion of the memory resources of the audience if it employs a spatial,
visual display in combination with the verbal discourse.
Robinson et al. performed research into this phenomenon by comparing graphic
organizers and concept maps with linear lists and outlines [94, 95]. Graphic organizers
are simply graphical layouts of information, for example tables and flowcharts. This
research suggests that the information in the graphic organizers and content maps is
encoded more spatially than the information in linear lists and outlines. In an earlier
study, Robinson et al. also investigated the benefits of adjunct displays in a setting more
comparable to that of presentations [94]. Subjects were shown different visual displays
while a related text was presented aurally. As in the other study, graphic organizers and
concept maps facilitated a more spatial encoding of the information than the textual
displays.
The spatial organization of data in ZUIs, though unconstrained, lends itself to
structures similar to graphic organizers and concept maps. As a result, ZUI presentations
likely allow for spatial memory encoding of the presentation layout. Combining this
spatial data with the conceptual encoding of the oral discourse may both reinforce the
conceptual structure and help reduce the audience' verbal load, ultimately increasing the
retention ofcertain presentation data.
One specific instance of this dual memory encoding is that, in certain situations,
the memory for data and the spatial location of that data are correlated (summarized in
[28]). For presentations, this implies that more memorable spatial layouts may improve
the memorability of the underlying presentation content. As a result, the potential
143
advantage of CounterPoint presentations over traditional slide shows is the ability to
spatially organize presentation structure in two dimensions at different magnifications.
This spatial layout may provide the audience with a strengthened memory pathway with
which to recall the presentation content.
4.3.1.2 Improved Overview Support
ZUIs naturally support overviews by providing the capability to zoom out for
more context. Good overviews may also benefit the audience by increasing the
memorability and possibly the comprehensibility of high-level presentation concepts. In
a hypertext reading study, spatial, hierarchical overviews of hypertext networks were
demonstrated to improve recall of overview titles when compared to both hypermedia
with alphabetical linear overviews and hypermedia without overviews [24]. Similarly,
overviews have been shown to improve the understanding of concept maps (see Figure
48) over several disconnected views in subjects with low spatial abilities [116].
Overviews in CounterPoint presentations also reveal spatial landmarks in the
presentation layout. Landmarks have been identified as a fundamental component in the
development or orientation knowledge [28]. Landmarks indicate a position in the global
surroundings based on salient or memorable objects in the local surroundings.
Consequently, CounterPoint overviews may also serve the purpose of helping the
presenter and audience stay oriented during a presentation.
144
Figure 48: An example of a concept map taken from Robinson et al. [95].
4.3.1.3 Structural Visibility
CounterPoint presentations allow the logical organization of the presentation to be
incorporated into the spatial layout of the data. Then, because the transitions between
viewpoints in CounterPoint presentations animate through the space, they reveal this
logical structure to the audience through the normal course of the presentation. Designers
have recognized the benefits of portraying the structure of an object to its users in this
manner. Norman describes the principle of revealing the structure of an object in its
design as "visibility" [77]. Similarly, Th?ring et al. suggest that presenting a hypertext
document's structure to the audience is a necessary component "for reducing the mental
effort of comprehension" [109]. A similar sentiment is offered by Rivlin et al. for
improving hypertext navigation [92].
Buffalo
North American
Bison
African Buffalo
Asiatic Water
Buffalo
10 Months
2500 lbs.
4.5 ft.
30 to 35 years
islands of
Southeast Asia
to Egypt
open grasslands
of the sahara
25 years
5ft.
2000 lbs.
11 months
9months
2800 lbs.
6ft.
Rocky Mountains
from Colorado to
subarctic Canada
20 years
Has Type
Gestation
Weight
Height
Lifespan
Habitat
Has Type
Has Type Habitat
Lifespan
Height
Weight
Gestation
Gestation
Weight
Height
Lifespan
Habitat
145
Structural visibility can also be important when a presentation shifts from one
topic to another. In this case, two adjacent slides in the presentation sequence may
contain no semantic relationship. In the slide show metaphor, the presenter was
responsible for orienting the audience to this kind of topic change. In contrast,
CounterPoint presentations can indicate the semantic difference between two slides by
the visualdistance in the presentations spatiallayout. This spatialseparation can then be
indicated to the audience through the overviews and spatial animations. A similar
example of semantic distance explored in hypermedia is the "warp coefficient" suggested
by Kaplan and Moulthrop [62]. Here a number is associated with each link on a
hypermedia page to indicate the semantic difference between the content of the current
page and the link's destination page.
4.3.1.4 Incrementally Revealing Content
One problem mentioned in the use of concept maps, a specific type of spatial
display shown in Figure 48, is map shock [12]. Map shock occurs when map-readers feel
overwhelmed, confused, or unmotivated by the size or complexity of a map. To solve
this problem, map content can be incrementally revealed to help make information appear
less intimidating.
An early technique used for incrementally revealing content is the stacked map
[116]. Stacked maps divide a large or complex concept map into smaller cross-
referenced maps. One study compared these stacked maps to un-segmented whole maps
to determine if they improve comprehension of map content [116]. The results of this
study suggest that the utility of different map formats may depend on subjects' individual
146
differences. Subjects with low spatial abilities performed better with whole maps while
subjects with high spatial abilities performed better with stacked maps.
A more recent technique, applicable to computer-based displays, that tries to
address some of the limitations of stacked maps is using animation to incrementally
reveal map content. Here, the map presents a subset of the map content and
incrementally animates more details into the display. A recent study compared these
animated maps against plain text, animated text, and static maps [12]. The results of this
study indicate that animated maps may facilitate better recall of high-level points than the
other three displays.
Because they use viewpoint animations, CounterPoint presentations can similarly
mediate between stacked and whole map displays by incrementally revealing content.
Meaningful chunks of presentation content can be arranged at different spatial locations
to achieve an effect similar to disjoint, stacked concept maps. Then, spatial animations
can be used to navigate between these disjoint maps in the 2D space. And because they
support zooming, CounterPoint presentations can display overviews of the collection of
stacked maps to also support whole-map displays.
4.3.1.5 Animated Slide Transitions
Although animated transitions are available in many slide show based
presentation tools, these transitions are mainly used for visual effect and usually do not
attempt to give any insight into the underlying data. CounterPoint presentations
implement slide transitions as animated viewpoint navigations through the presentation
space. As such, these animations are able to display the changing spatial context as the
system transitions from one point in the 2D space to another. Although the actual
147
benefits of viewpoint animation still require further investigation, initial research
indicates that these animations are beneficial for learning spatial organizations and data
relations [7]. This study further suggests that viewpoint animations allow for a more
constant understanding of object positions and relationships than viewpoint transitions
without animation. Research also indicates that animation may improve long-term
understanding of various kinds of presented material [50, 65]. This improvement was
most profoundly observed in those with low spatial abilities [50].
One of the biggest risks associated with animations is the time consumed by
presenting extra intermediate frames during a transition. However, research also indicates
that the extra time spent on animation does not result in longer task completion times [7],
which relates directly to comprehension time.
Of course, animated transitions also pose more subjective risks as well. For
instance, some users may find these animations distracting or otherwise undesirable.
However, there is some evidence to suggest a subjective preference for animated systems
over non-animated systems [29]. Although user preference is a recognized quantitative
measure of software usability [105], these preferences require further study to determine
their importance, both in general and specifically for presentations.
The animated transitions in CounterPoint presentations may also benefit from
additional dual-encoding redundancy. Research has shown that animations accompanied
by explanatory audio can improve understanding of abstract concepts over static graphics
combined with explanatory audio [65]. This animation-audio combination also led to
better long-termretention than its static counterpart [65].
148
It is also important to note that there are a number ofdifferent types ofanimations
(see [7] for a partial listing) that can make comparisons difficult across tasks. Similarly,
whether a system's animation is automated or manually controlled can affect its cognitive
utility. Therefore, these differences must be considered when comparing the results from
other animated systems to the ZUI presentation setting.
4.3.2 An Audience Study
Based on this previous research and our own experience, we hypothesize that
CounterPoint presentations offer advantages to the audience for recalling information
from the presentation. In particular, we anticipated that these presentations would help
the audience remember the main ideas and the high level structure.
In designing a studyto test this hypothesis, it was noted that CounterPoint
presentations differentiate themselves from the traditional slide show metaphor through
the two features of spatial layout and animation. The spatial layout is distinguished from
spatial layouts on individual slides in that all of the information on all of the slides is
positioned in a single continuous space. Animations are then used in moving between
points in this space to visually reinforce the sense of physical motion. Consequently,
these features were evaluated in the user study below, comparing their effects on
subjective preferences and perceptions as well as recall for content and structure.
4.3.2.1 Method
Participants in the study consisted of 96 undergraduate and graduate students in
the University of Maryland computer science department. Seventy-eight of the students
were male and 18 were female. Participants were all enrolled in one of three upper level
149
undergraduate Human-Computer Interaction courses. Each of the 3 classes was assigned
to one of the following 3 conditions: 1) a traditional slide show, 2) a traditional slide
show plus spatial layouts and overviews, and 3) a traditional slide show plus spatial
layouts, overviews, and animations between spatial positions. These conditions are
showninTable7below:
Table 7: Conditions explored in the study. Animation and no spatial layout were not
explored since the animations were not expected to contribute to non-spatial
presentations.
No Animation Animation
No Spatial Layout Standard PowerPoint These animations indicate only
forwards or backwards progress
Spatial Layout CounterPoint without
animated transitions
Standard CounterPoint
In the above table, the condition employing a traditional slide show and
animations but not spatial layout was not studied because it did not seem like a
distinguishable difference from an ordinary slide show. In addition, to make the
traditional slide show comparable to the spatial layout conditions, linear overviews were
added at each point where a spatial overview was shown in the spatial conditions.
Examples of the top level overviews are visible in Figure 49. Each of the 3 classes was
then shown a presentation using the features of the assigned condition. The actual slides
for each condition were identical. The slides were shown in the same order and at the
same times in all conditions. Similarly, the visual presentation was accompanied by a 10
minute pre-recorded audio track that was identical for each condition. The topic of the
presentation for all conditions was genetically modified crops.
150
Figure 49: A top level overview for the conditions in the study. The left image is the
overview in the no spatial layout condition. The right image is the overview for both of
the spatial layout conditions.
Following the presentation, participants were asked to fill out a questionnaire
measuring subjective ratings of the presentation as well as the participants recall for
content and structure. In the next class period, participants were also given a second
questionnaire that again measured the participants recall for content and structure. In
each condition, the follow-up questionnaire was administered following a 2 day break.
The questionnaire had two content related sections. The first section asked them
to recreate an outline of the presentation. The outline section was scored using two
scores of content and structure. The content score awarded 1 point for each correct item
and 1 point ifthe itemappeared in the correct position in the outline. The structure
measure awarded 1 point for each correct outline position that was filled, regardless of
thecontentoftheiteminthatposition. Overall,therewere18possibleoutlineitems.
This resulted in a maximum possible outline content score of 36 and a maximum possible
151
outline structure score of 18. This question had the same point value in both the initial
and follow-up questionnaires.
The second content section asked questions about specific facts in the
presentation. Each of these questions had a different point value based on the number of
possible responses. The total number of possible points in the initial questionnaire was
20 while the total in the follow-up questionnaire was 10.
The questions regarding subjective ratings of the presentations used a scale of 1 to
9 and covered four aspects of the presentation including: the overall visual portion of the
presentation, the presentation's organization, the presentation's ability to convey the main
areas covered, and the presentation's ability to convey the current progress of the
presentation. The full questionnaires for both the initial and long-term sessions can be
seen in Appendix A.
4.3.2.2 Results
One-way ANOVAs with independent measures was used to analyze the results in
preference to a t-test since the study compared 3 study conditions. Post hoc tests were
then used to reduce the chances of Type I error in performing multiple comparisons. In
particular, Scheff? tests were used as they are conservative yet more sensitive for
complex (in addition to pairwise) comparisons between conditions than Tukey tests.
The data for 28 of the 96 participants involved in the study was discarded because
the subjects did not complete the content portions of one or both questionnaires. Of the
remaining subjects, 36 received the no spatial layout, no animation condition, 14 received
the spatial layout, no animation condition, and 18 received the spatial layout, animation
152
condition. The remaining subjects consisted of 55 males and 13 females. The means for
dependent variables measured in the study are shown in Table 8.
The one-way ANOVAs on the remaining 68 subjects showed a significant effect
ofpresentation type on the questionnaire items regarding the overallvisualportion ofthe
presentation (p < 0.01, F(2,65)=6.03) and the presentation's organization (p < 0.05,
F(2,65)=3.24). Post hoc Scheffe test indicated that the spatial layout conditions were
significantly higher ranked than the traditional slide show condition on both questions
with p<0.01 and p<0.05 respectively. Figure 50 compares the means and standard
deviations for the responses to these two questionnaire items.
Table 8: The means and standard deviations for the dependent variables measured in the
study.
No Space,
No Anim
Space,
No Anim
Space,
Anim
Liked Visuals (1-9) 4.81
(s 1.82)
6.21
(s 1.25)
6.17
(s 1.50)
Organization (1-9) 7.14
(s 1.29)
7.86
(s 0.77)
7.83
(s 1.04)
Main Topics (1-9) 7.81
(s 1.26)
8.00
(s 1.41)
8.22
(s 0.73)
Progress (1-9) 7.28
(s 1.26)
7.71
(s 1.07)
7.44
(s 1.38)
Specific Content (0-20) 10.33
(s 2.89)
9.96
(s 3.63)
8.44
(s 2.89)
Outline Content (0-36) 11.14
(s 4.09)
12.29
(s 3.67)
12.56
(s 2.77)
Outline Structure (0-18) 6.14
(s 2.55)
7.14
(s 3.23)
7.11
(s 2.14)
LT Specific Content (0-10) 3.75
(s 1.46)
3.68
(s 1.73)
3.91
(s 1.48)
LT Outline Content (0-36) 10.94
(s 4.88)
13.14
(s 5.49)
11.44
(s 1.76)
LT Outline Structure
(0-18)
5.81
(s 2.87)
8.14
(s 4.00)
6.72
(s 2.52)
153
ANOVAs did not indicate a significant effect of presentation type on the
questionnaire measures for recall of content or structure. However, as seen in Figure 51,
there were emerging patterns in the outline data suggesting higher values for the spatial
layout conditions. An inverse tendency appeared to hold for the specific questionnaire
items shown in Figure 52. Because no significant effect was found for these scores,
inter-rater reliability was not calculated for these questions.
Figure 50: The graph on the left compares the means and standard deviations for
responses to the questionnaire item?Rate how much you liked the visualportion ofthe
presentation?? The graph on the right compares the means and standard deviations for
responses to the questionnaire item ?How would you rate the presentation?s
organization??
6.26.2
4.8
0
1
2
3
4
5
6
7
8
9
PowerPoint CounterPoint CounterPoint w/o
Anim
O
v
e
r
all
R
atin
g
(
1-10)
7.867.83
7.14
0
1
2
3
4
5
6
7
8
9
10
PowerPoint CounterPoint CounterPoint w/o
Anim
R
a
ti
ng
o
f
O
r
g
a
ni
z
a
t
i
on
(
1
-
9
)
154
Figure 51: The graph on the left shows the short and long term recall of outline content.
The graph on the right shows the short and long term recall of outline structure. These
results indicate a possible pattern in the data but they were not significant.
Figure 52: A graph of short and long term recall of specific content questions. These
resultsindicateapossiblepatterninthedatabutwerenotsignificant.
10.33
3.75
9.96
3.68
8.44
3.91
0
2
4
6
8
10
12
Short Term Long Term
De
t
a
i
l
Re
c
a
l
l
PowerPoint
Spatial Layout, No Anim
Spatial Layout, Anim
10.94
12.56
12.29
11.14
11.44
13.14
0
2
4
6
8
10
12
14
Short Term Long Term
O
utline
C
ontent
(0
-
3
6
)
PowerPoint
CounterPoint
CounterPoint w/o Anim
6.14
5.81
7.11
6.72
7.14
8.14
0
1
2
3
4
5
6
7
8
9
Short Term Long Term
O
utline
S
tr
u
c
t
u
r
e
(0-1
8)
PowerPoint
CounterPoint
CounterPoint w/o Anim
155
4.3.2.3 Discussion
The study revealed a general preference for the presentations with spatial layout
over the more traditional slide show presentation. This could be an indication of the
prevalence and resulting monotony of slide show presentations. Indeed, two-thirds of the
participants indicated having seen 20 or more PowerPoint-style presentations in the last
year. However, this novelty effect may also reflect in part the novelty of the spatial
layout. One of the ancillary benefits of spatial layout over a linear slide show is the
ability to have unique spatial layouts for each presentation. In either case, the between-
subjects conditions helped reduce novelty effects as much as possible since subjects
could not make comparisons between conditions.
The data analysis also indicated a significantly higher ranking of the organization
of the spatial layout conditions. While this again may be related to a novelty effect, it is
also supported by the pattern shown in Figure 51 in recall for information in the outline.
Interestingly, this pattern seems to appear in recall for both content and structure of the
outline. This lends support to our hypothesis that the audience may be benefiting from a
kind of dual memory encoding in recalling high level presentation information.
The previous result is particularly significant given the artificiality in the number
of overviews shown in the no spatial layout and no animation condition. Although the
number of overviews was constant between all the conditions, overviews often felt
unnatural when they were shown in this more traditional slide show context because they
appeared as repetitive outline slides. In contrast, the overviews were more naturalin the
ZUI conditions since they were revealed as part of the navigations through the
presentation space.
156
The pattern shown in Figure 52 for recall of specific content may be yet another
artifact of the novelty effect. The fact that the lowest scores occurred for the spatial
layout and animation condition is consistent with this explanation since this condition
was the most different from a traditional slide show. A similar effect has also been noted
by several presenters in using CounterPoint for real presentations. These presenters have
noted an initial shock among audience members when using CounterPoint, which causes
them to miss the content in the first few minutes of the presentation. This kind of
extreme reaction is likely to diminish over time as CounterPoint style presentations
become more common.
In general, it was not surprising that the study did not reveal a significant effect of
presentation type on the recall measures. One of the biggest reasons was because the
presentation was extremely short at only 10 minutes long. Further, the overviews and
transitions between slides comprised only a small fraction of this already short time. This
meant that the conditions in the study differed for only about a minutes worth of time.
An additional reason for the lack of effect was the large variability between subjects.
Subjects varied in their prior knowledge of genetically engineered crops and also in their
spatial and verbal abilities.
4.4 The Presenter?s Perspective
Beyond the above cognitive benefits, CounterPoint presentations also offer a
number of more concrete advantages for presenters over traditional slide show
presentations. In fact, CounterPoint?s greatest advantages are likely to be for the
presenter rather than the audience. Below, the novel affordances of CounterPoint
157
presentations are discussed for some common presentation authoring and navigation
challenges.
4.4.1 Enabling CounterPoint Features
4.4.1.1 Hierarchical Support
One of the problems in using the slide show metaphor is breaking a complex set
of ideas into a linear list of slide-sized chunks. Presenters often prefer instead to organize
their presentations using a hierarchical structure [55]. The CounterPoint?s ZUI metaphor
provides natural support for hierarchies by facilitating a spatial portrayal of hierarchies
that mimics a 2D projection of a physical tree (for examples, see [52, 85]). In addition,
ZUIs allow for visually distinguishing hierarchy levels by placing them at varying levels
of scale or magnification. This change in magnification can decrease uniformly between
levels of detail to indicate the level of nesting consistently across the presentation.
Of course, this support for hierarchies is also useful because the structure can be
shown to the audience during the presentation. This frees the presenter from having to
create and maintain overview slides. It can also make it easier to portray complex
overviews at multiple levels of detail.
4.4.1.2 Spatial Navigation for Improvisation
CounterPoint?s ZUI metaphor is based on physical space. As a result,
CounterPoint offers navigation that mimics physical navigation. This type of navigation
directly addresses one of the primary weaknesses in the slide show metaphor, namely the
ability to jump to arbitrary points in a presentation with minimal interruption. These
kinds of jumps can become necessary to address audience questions, unexpected time
constraints, or other impromptu situations.
158
While this type of navigation is possible in the slide show metaphor, it incurs a
fairly high interruption cost for presentations because of the linear nature of the
navigational search. Specifically, presenters are often forced to sequentially step through
a subset of their slides when searching for a specific bit of information in a traditional
slide show. This becomes particularly problematic in presentations with a large number
of slides. In contrast, CounterPoint provides mechanisms for organizing information at
multiple levels of detail so that the number of possible navigation targets at any point in
the presentation is reduced, logarithmically in the case of a hierarchical organization.
This problem can also be described in terms of the structure of the presentation.
In the slide show metaphor, no structure is imposed on the slides other than their linear
ordering. The slides are primarily discrete, self-contained units with no knowledge of
relationships between slides. In contrast, CounterPoint allows the presenter to use the
presentation's structure to organize information beyond slide boundaries. As a result, the
presenter's navigation decisions in the zooming space are informed by spatial and
structural cues from the inter-slide relationships. The overall result is that the complexity
of navigation decisions is reduced.
Zooming navigation also supports the presenter's desire to project a consistent and
professional appearance. This is accomplished by eliminating presentation-irrelevant
visual controls for navigation, such as traditional GUI widgets. Because the ZUI
paradigm is based on navigating a single continuous space, it is able to avoid dialogs,
menus, and scrollbars in favor of consistent navigation cues, gestures, and keystrokes.
These navigation controls are also compatible with controls for traditional rehearsed
scripting navigation, such as those found in existing software presentation tools.
159
4.4.1.3 Separation of Path and Content
A common presentation task is to reuse an existing presentation for impromptu or
otherwise unanticipated settings. This typically means adapting the presentation on the
fly to audience interest or different time constraints. The slide show metaphor is not well
suited to this type ofspontaneityas the content ofthe presentation is directlytied to the
order of presentation. Restated, the slides represent both the information a presentation
will contain and the rhetorical structure the story will follow. In contrast, CounterPoint
presentations separate the content of the presentation from the path through that content.
This means that the presentation material remains constant independent of the specific
routes through that material. This parallels the physical world in that multiple paths or
tours are available through the same physical locations. The separation allows for
creating traditional scripted paths and tours but also facilitates improvised storylines
through dynamic navigation in an existing presentation space.
Making additional unvisited information visible in the structure of the
presentation has an additional benefit for many presentations in that it can help spark
audience discussion or inform audience questions. Specifically, it can suggest to the
audience where additional information is available with further probing.
4.4.1.4 Visual Support for Transitions
One failing of the slide show metaphor is in providing visual support for the
presenter during transitions. The ZUI metaphor addresses this issue through the use of
animated spatial navigations between locations in the zoomable space. Because the
zoomable space is used by the presenter to portray the structure of the presentation, this
structure is revealed to the audience during the animated navigations through space. As a
160
result, the presenter's transitions between points in the logical structure of the
presentation are supported visually by the animated transitions between points in the
spatialstructure ofthe presentation.
4.4.1.5 Conveying Presentation Progress
The slide show metaphor has no inherent notion of presentation progress. One
common technique presenters use to compensate for this deficiency is to manually add
text specifying, "Slide n of N," to convey an overall progress value. However, such a
display does not indicate more localized progress, such as the number of slides remaining
in the current topic.
Through the use of a spatial metaphor, CounterPoint presentations have a natural
indication of presentation progress. In particular, the spatial metaphor can indicate
CounterPoint can also provide a more explicit indicator ofprogress byvisually
altering visited slides. This concept builds on the concept of visited hyperlinks in a web
browser. In our own use of CounterPoint, we have found the combination of these
implicit and explicit progress indicators generally effective at conveying progress.
4.4.1.6 Creative Control
Because they support the arrangement of presentation content in a two and half
dimensional space, ZUIs offer an additional degree of creative freedom over the discrete
boundaries inherent in the slide show metaphor. One example that demonstrates the
creative potential of the medium is described in the original Pad research [85]that
overlaid text passages on an image of a tree. Another example is described in Gray
Matters [114] in which information is laid out in relation to various images of body parts.
161
In addition, unlike other novel user interface approaches such as Hyper Mochi
Sheet [111], CounterPoint presentations also support deterministic control over
presentation layouts and transitions. This type of direct control ensures predictability,
which authors are likely to expect for presentations.
4.4.2 Real World Experience
As discussed above, CounterPoint offers a number of potential benefits to the
presenter. However, the effect of these benefits can be subtle because they are only
exhibited in the transitions between slides, which are often a small part of a larger
presentation. Consequently, any improvements in these transitions can be difficult to
measure through controlled evaluations. Nonetheless, in our own use of CounterPoint,
we have found these benefits to be compelling. In order to see if these benefits
compelled others, we made CounterPoint available to the public for use in actual
presentations. A version of CounterPoint has been available for download from the web
since February 2001.
4.4.2.1 External Feedback
We collected feedback from 7 presenters who had used CounterPoint to deliver
from 1 to 50 presentations. The total number of presentations delivered was 73, resulting
in an average of 10 presentations per presenter and a median of 3. The presenters were
employed primarily at educational or research institutions although some of the
institutions had military and intelligence ties as well. All seven presenters either worked
outside of the University of Maryland and the Palo Alto Research Center or were not
directly involved in developing CounterPoint. Four presenters were interviewed in
person or over the phone and the other three filled out a questionnaire on the web.
162
Presenters were asked to describe the features and benefits that had motivated
their use of CounterPoint or that they had discovered through using CounterPoint. The
most common response, given in some form by all 7 users, was the ability to portray
overviews or multiple levels of detail. In describing this capability, six of the presenters
specifically appreciated CounterPoint?s hierarchical or ?clustered? organizations and its
ability to portray this structure to the audience. Related but less frequently noted benefits
included portraying progress to the audience, keeping the audience oriented to the current
location in the talk, and indicating the number of slides at each level of the talk.
Two of the more experienced CounterPoint presenters also valued CounterPoint?s
ability to keep the audience oriented during unscripted navigations. They noted that
when jumping out of order in CounterPoint to answer questions, the audience is given
more visual cues, through the overviews and animations, as to where the presenter is
going in their talk. More of the presenters probably did not comment on this aspect of
CounterPoint since they indicated that they had not discovered or made use of the
navigation controls available during presentation delivery.
Presenters also indicated a number of significant challenges in authoring and
delivering presentations with CounterPoint. In fact, it is a testimony to the power of ZUI
presentations that presenters used CounterPoint in spite of these difficulties. Some of the
authoring challenges noted by presenters included tuning layouts, compensating for
missing animation features from PowerPoint, and synchronizing between PowerPoint and
CounterPoint. Several presenters also found flexibility problems in adapting
CounterPoint to their presentation style. In particular, two presenters wanted to be able to
hierarchically nest one slide inside of another, which was not supported very well in
163
CounterPoint. Many of the presenters also described problems in either finding or using
CounterPoint?s navigation controls during presentation delivery. A common fix for
navigation suggested by several presenters was to have navigation icons on the screen
during delivery so presenters would not have to remember mouse gestures or key
commands.
4.4.2.2 Our Experience
We have also used CounterPoint extensively ourselves for delivering
presentations. Much of our experience echoes the experiences described by external
presenters above. However, we have found that one of the most promising applications
of CounterPoint is for more interactive presentations with a lot of information. These
kinds of presentations can be characterized as ?briefings? since the presenter is
condensing a large body of work under limited time constraints. Often the presenter is
not be able to predict or doesn?t have time to prepare the specific subset of information
that the audience will be interested in from the large collection.
In this vein, Mark Stefik, the author?s supervisor and a lab manager at Palo Alto
Research Center, has created a presentation with 101 slides that describes various
organizational aspects, research areas, and business issues related to the Information
Sciences and Technology lab. Mark estimates that he has delivered this large
presentation over 50 times on various road trips and for visitors in the lab. Since there
are a lot slides and each audience has only limited interests, Mark customizes the
presentation on the fly based on what he knows of the audience and the feedback they
provide. An overview ofa version ofhis presentation is shown in Figure 53. It should be
noted that although the visual design could be improved, it serves its purpose since it is
164
combined with animated transitions, multi-level overviews, and Mark?s ongoing
commentary.
We have found that the use of CounterPoint in briefing scenarios resonates with
intelligence and military personnel. These organizations seem to be particularly in tune
with this use of CounterPoint since they have documented problems in managing the glut
of presentation slides [58]. One member of our lab describes CounterPoint?s benefits for
these organizations in terms of the ?bionic analyst.? The bionic analyst creates a broad,
comprehensive CounterPoint presentation with all potentially relevant information and
then smoothly navigates the expanse in response to audience interests and questions.
Figure 53: A version of Mark Stefik?s lab overview presentation in CounterPoint.
165
Chapter 5:
CounterPoint Features and Implementation
5.1 Implementation
5.1.1 Architecture
CounterPoint is built on top of Jazz [10], a Java toolkit for building ZUIs, and is
currently available for download on the web (http://www.cs.umd.edu/hcil/counterpoint/).
In building CounterPoint, we wanted to take advantage of existing presentation tools.
Although there are currently a handful of commercial slide show presentation tools
available, the tool that clearly dominates the market is Microsoft PowerPoint [89].
Therefore, to have the greatest potential impact on presentation authors, CounterPoint
was created as a plug-in to PowerPoint. This allowed CounterPoint to leverage
presenters? existing slide authoring experience since many presenters are already using
PowerPoint for presentations. In addition, it also allows presenters to reuse existing
slides that have been authored and saved in PowerPoint?s specific format. Most
importantly, extending PowerPoint greatly reduced the functionality needed in
CounterPoint.
CounterPoint uses VisualBasic's COM hooks into PowerPoint to add a toolbar
button and manipulate slide content. Because the majority of CounterPoint is built on top
of Jazz in Java, one of the Visual Basic application's primary responsibilities is to start a
Java application when its toolbar button has been pressed. Its other major responsibility
is to start a TCP/IP client by which it will communicate with this Java application.
Similarly, the first responsibility of the Java application is to create a TCP/IP
server to communicate with the Visual Basic component. TCP/IP was chosen for inter-
166
process communication because of its simplicity. Once a connection has been
established between the two processes, the PowerPoint slide contents are transmitted to
CounterPoint. For convenience reasons, the slide contents are not transmitted via the
TCP/IP connection but are passed instead via the Windows clipboard.
This transfer of PowerPoint slide contents is possible because PowerPoint uses
the Windows metafile format (i.e. files with a list of drawing commands) for posting to
the clipboard, in addition to their proprietary file format. In addition, this metafile format
also has benefits for our application since Windows provides native support for metafile
rendering.
Consequently, a third component of our application is implemented in Windows
native code for managing and rendering Windows metafiles. Our Java code uses the Java
Native Interface (JNI) to communicate with the native code and to switch between native
and Java rendering as appropriate.
The CounterPoint portion ofthe presentation data, such as slide border colorings,
spatial layout parameters, and path orderings, are currently stored in a custom XML file
residing in the same directory as the PowerPoint file. Because the format is XML, the
file can be manually edited in a text editor in cases where the CounterPoint data has
become out ofsync with the PowerPoint presentation or for finer grain controlover
certain parameters.
In total, CounterPoint is comprised of 83 source files representing 292 java
classes. The total number of lines of code in all source files is around 15K not including
comments and around 19K including comments.
167
5.1.2 Rendering
One of the implementation issues given significant attention in CounterPoint was
rendering PowerPoint slides on the zoomable Jazz surface. As mentioned, the slide data
is obtained from PowerPoint in Windows metafile format. Windows metafile format is a
vector graphics format used to reduce aliasing effects when rendering at different levels
of magnification. One consequence of this vector format is that the system is often
required to render many small visual objects for each individual PowerPoint slide.
Consequently, when many PowerPoint slides are shown in a single CounterPoint
overview, the rendering performance can become too slow for interactive animation
frame rates.
CounterPoint addresses this problem by using cached images of the metafiles
during animations and user interactions. This results in a fixed rendering time for each
slide regardless of content. While this improves performance, it also introduces aliasing
effects when the image is rendered at resolutions other than 1-to-1. To reduce these
aliasing effects, CounterPoint uses a simple image pyramid with cached images of the
metafile at multiple resolutions. The current CounterPoint version uses a fixed number of
images in its image pyramid but future versions could take into consideration the number
of slides in the presentation, the resolution of the display, the amount of memory on the
machine, or the number of other applications running. After the initial image pyramid
rendering during animations or user interactions, CounterPoint re-renders each of the
affected slides in a background thread using the actual Windows metafile to further
eliminate aliasing effects.
168
The previous metafile-based rendering approach works well for static content but
does not work for animated PowerPoint slides. One experimental solution used in
CounterPoint to support animated PowerPoint slides is displaying the actual PowerPoint
window on top of the CounterPoint window. The PowerPoint window is shown when a
slide is brought into focus and then during the slide?s animations. The CounterPoint
window is brought back into focus during transitions between slides or to show
overviews. In order to eliminate flashing when switching between the two windows,
both windows are set to be ?layered? which ultimately means that the full window
contents are cached as images in memory. This feature is only supported in Windows
2000 or later.
5.2 Features
5.2.1 Layout
CounterPoint distinguishes the conceptual structure from the spatial structure by
providing tools for organizing slides into a conceptual hierarchy. Spatial layouts can then
be automaticallygenerated using the containment relationships fromthe conceptual
structure. From this initial automated layout the presenter is able to manually modify
parameters of the automatic layouts as well as edit the spatial layouts for more direct
control. Using the conceptual structure also allows automatic layout parameters to be
propagated down the hierarchy to give all members of a conceptual group a consistent
visual appearance. Currently CounterPoint primarily uses the connectedness and
proximity principles from the gestalt theory of perception to indicate conceptual
relationships (see Figure 54). However, additional perceptual principles such as color
similarity or closed forms can also be added to further emphasize group membership.
169
When CounterPoint loads a presentation for the first time, the presentation?s
slides are arranged in a grid within CounterPoint?s zoomable space. Hence, the typical
first step in creating a presentation in CounterPoint is to modifythe arrangements ofthe
PowerPoint slides in the zooming space. To arrange slides manually, CounterPoint
provides simple tools for manipulating objects in this space similar to those found in
PowerPoint, drawing programs, and previous zoomable demo programs (e.g., PadDraw
[9] and HiNote [10]).
Figure 54: CounterPoint in layout organizer mode. This allows the author to create text
labels and define a hierarchy with the slides.
CounterPoint also provides a hierarchy editor, shown in Figure 54, for automating
these arrangements. Using this editor, the author can organize the presentation contents
170
into a semantically meaningful hierarchy. Then, for each parent in the hierarchy, the
author can apply a modifiable layout template to spatially arrange the parent?s children
according to the template format. Currently, CounterPoint provides layout templates
corresponding to geometric shapes, such as lines, ellipses, arcs, and rectangles. For
example, the slides that are children of the label ?Version 2? can be automatically
arranged using an arc layout to achieve this effect.
5.2.2 Path
The next step in the authoring process is to create paths through the presentation
space. CounterPoint?s path editing mode is shown in Figure 55. When CounterPoint
loads a presentation for the first time, a single default path is automatically generated that
visits each of the PowerPoint slides. In general, these paths are composed of two types
of components. The first, more obvious type is the actual PowerPoint slide, which is
inserted on a path to animate the slide to full screen size. These slides are inserted into
the path using a simple scrolling list of thumbnails. Each slide can also be inserted
multiple times in a single path.
The second type of path component is a view of a particular region of the
zoomable space. These views are the more interesting path component as they allow the
author to include views containing multiple slides and the structure of the presentation.
Views are useful for showing an overview of the entire presentation or focused overviews
of particular subsections of the presentation.
The current mechanism used to create these types of views is similar to taking a
picture or creating a screen snapshot. First, the author navigates to the particular region
of space to be added to the path. The author then presses the camera toolbar button (see
171
Figure 55) and a new component, represented by a thumbnail image of the view, is added
to the path. These thumbnails are actually implemented as live views onto the
presentation space so that modifications to the zoomable space are reflected in the
thumbnail.
Figure 55: CounterPoint in path editing mode. The panel on the left represents the
sequence of slides and views in the current presentation path.
While a one-dimensional representation of the current path is available in path
editing mode, CounterPoint also provides a two-dimensional path editor, shown in Figure
56, that mimics the functionality of PowerPoint?s slide sorter. This will likely allow for
the transfer of pre-existing PowerPoint skills since the concepts of path editing and slide
sorting are so similar.
172
In addition, paths can be created automatically in CounterPoint using both
conceptual and spatial structure. CounterPoint uses a default spatial ordering algorithm
that is intended to follow the reading conventions of the culture. For western culture, this
convention suggests an order from top to bottom and left to right. The algorithm also
uses conceptual structure to compute a depth-first ordering of the hierarchy with
overviews added for transitions between hierarchy levels or groups. Naturally, this depth-
first traversal could also be manually modified to use a breadth-first or other tree traversal
at any point in the hierarchy. CounterPoint represents the path as a set of viewpoints that
resembles a traditional slide show. As a result, the path can be modified using techniques
similar to those used in traditional software presentation authoring tools.
Figure 56: CounterPoint?s 2D path editor that mimics PowerPoint?s slide sorter.
173
5.2.3 Presentation Delivery
Perhaps the most interesting and novel interactions occur in CounterPoint?s
presentation mode (Figure 57). The default behavior of sequentially stepping through
one of the author?s predefined paths is still available. This default behavior is achieved
with the standard PowerPoint controls ofleft mouse button, the space bar, the page down
key, or right arrow key on the keyboard.
Figure 57: A screen shot of CounterPoint in presentation mode. Here, the presenter can
alter pre-scripted traversals using various presentation-time interactions. Black borders
indicate slides already visited during the presentation. The current focus is highlighted in
red.
174
However, CounterPoint offers two modifications to this standard interaction for
improvisation. First, the presenter can press the up arrow key to navigate up the
previously defined hierarchy. This zooms out enough to get an overview of a
semantically meaningful group of slides. If the layout hierarchy has not been defined,
pressing the up arrow key zooms out to give an overview of the entire space.
A second interaction allows a presenter to dynamically navigate to various
interesting locations in the presentation. However, before navigating to a target location,
the presenter must navigate to an overview where the location is visible. This is typically
achieved by zooming out using the up arrow key. For immediate access to a PowerPoint
slide, left clicking on the slide animates the view to that location. Similarly, when the
presenter moves the mouse within the bounds of a sub tree, the bounds of the target view
highlight. Left clicking within these highlighted bounds navigates to that location.
In cases where a presenter alters the presentation path using one of these dynamic
navigations, the systemattempts to pick an appropriate point in the path fromwhich to
resume. In cases where the target appears in multiple places on the path, CounterPoint
picks the path entryclosest to the point at which the presenter deviated fromthe path. If
the slide does not appear at all in the current path, the system does not try to infer a new
path entry but rather resumes from the point at which the presenter deviated from the
path.
One other traditional hypertext element that was added to CounterPoint to
improve usability is visited colorings. CounterPoint provides modifiable slide border
colorings to indicate which slides have been visited during a presentation. We have
found these colorings to be useful both for the presenter and the audience for providing
175
feedback as to which slides the presenter has visited and to give a sense of the overall
progress ofthe presentation.
176
Chapter 6:
Future Work
6.1 Authoring Presentation Content
Our understanding of a presentation authoring task was refined through 3
iterations of building and evaluating a software authoring tool. Through this iterative
process, a number of areas for further work were identified. Several features and
interaction techniques in Niagara need further development. The scope of authoring
tasks supported in the tool also needs to be expanded. In addition, many aspects of the
authoring process need further empirical study and the new interaction techniques need
more real world use.
6.1.1 Niagara Implementation
Niagara introduced a number of unique features that suggest areas for further
exploration. One technique that needs further work is automatic text reduction. There is
a range of different algorithms for reducing text from the simplest word based techniques,
implemented in Niagara, to complex techniques based on natural language semantics.
These different techniques need to be tested with different document types to see how
they interact with variations in content and document length. In addition, automatic text
reduction could also be integrated with other text visualizations, such as enhanced
thumbnails, to preserve different kinds of distinct formatting cues and document layouts.
Similarly, automatic text reduction could also be improved to take into consideration the
grouping structure to improve text views. For example, Figure 39 shows a number of
overlapping text boxes that could be aggregated to display a more useful view for the
group as a whole.
177
A second technique in need of further development is automatic grouping. One
immediate feature suggested by Niagara?s current implementation is the ability to create
groups of groups. This would allow hierarchical organizations that will be essential to
larger and more complex authoring tasks. Nevertheless, extending automatic grouping to
multiple levels may introduce conceptual challenges for the user since groups are created
and destroyed solely through object positions. Niagara?s automatic groups also suggest a
vast space of possible visual group designs such as different title and boundary
decorations. However, these different designs must be chosen carefully since they can
impact the user?s expectations in interacting with the groups.
Niagara is currently limited to organizing simple, segmented text based content.
A natural area for further investigation is incorporating different object types such as
multi-page documents, images, diagrams, and digital ink input. Niagara also
concentrates on the organization component of authoring. However, Niagara introduces
a natural platform to support additional authoring tasks such as building more formal
argument structures or designing other kinds of visual relationships.
A more practical area in need of further work is integrating the Niagara?s
functionality with existing presentation and office applications such as PowerPoint and
Word. Because Niagara creates a hierarchical structure, it could also connect more
directly to our work in authoring hierarchical presentations in CounterPoint.
6.1.2 Evaluation
The user studies in this work have been intended primarily to get at the high level
challenges in authoring presentations on a limited size computer display and whether
ZUIs address these challenges. As a result, they have not focused on the individual
178
effects of the interface techniques that were used to make ZUIs usable. Instead, this work
suggests a number of follow up studies to determine the optimal ZUI navigation
interactions, the most useful semantic zooming representations, and the best techniques
for transitioning from informal to formal structure. Our work also indicates that different
kinds of users are likely to benefit from different interface techniques. More work needs
to be done to determine the specific individual differences that are driving these
differences.
The authoring techniques described here also need more real world testing. This
testing will help determine the class of tasks for which a Niagara-like interface is
appropriate and those for which it is not. Real world testing for large amounts of
information will also help identify the scale limitations where these techniques break
down.
6.2 Presentation Delivery
Much of what was learned about presentation delivery came from having a
concrete prototype that integrated with an existing presentation tool. This allowed us to
gather a large amount of feedback to guide future development. The majority of what is
needed for ZUI presentations to gain wider acceptance is engineering to take our research
prototype and turn it into an actual product. However, our work has also raised a number
of interesting questions that need further investigation.
6.2.1 CounterPoint Implementation
One of the practical concerns in promoting CounterPoint to a wider audience is
minimizing the effort needed to author these presentations. Our existing CounterPoint
179
implementation requires the presenter to individually attend to the hierarchical structure,
spatial layout, and scripted traversal in order to obtain a usable presentation. A more
ideal CounterPoint design would require the presenter only to specify the presentation?s
hierarchical structure. Default versions of the spatial layout and scripted traversal would
then be created automatically based on this hierarchical structure. The spatial layout and
scripted traversal could later be manually modified if the defaults are not satisfactory.
Figure 58 below suggests integrating a hierarchical structure with an existing slide
authoring tool. Integration would provide a single unified structure that could facilitate a
more natural ZUI presentation tool. This single structure could also eliminate many of
the synchronization issues that currently plague CounterPoint. An integrated tool would
also allow for combining advanced slide features such as within-slide animations with the
spatial animations in ZUIs.
Figure 58 shows an example of an interface design for creating a presentation?s
hierarchical structure. The interface is much the same as a standard slide show editor but
provides facilities for hierarchically grouping slides. The example design mimics the
behavior of a graphical file browser but other designs, such as a textual outline editor, are
also possible. Apple?s Keynote presentation software already provides tools for
hierarchical slide organization [63].
Once the presentation?s hierarchical structure has been specified, our envisioned
tool could generate an automatic spatial layout. Two example automatic layouts are
shown below including a rectangular layout (Figure 59) and a network layout (Figure 60).
These automatic layouts can also serve as a starting point for more manual control.
180
Specifically, colors, layout shapes, container properties, label sizes, and label positions
can all be manually modified from the initial automatic layout.
Figure 58: An example interface for hierarchically organizing slides.
From the presentation?s hierarchical structure and spatial layout, a default
hierarchical traversal can be automatically generated. This invention proposal uses a
default spatial ordering algorithm that is intended to follow the reading conventions of
the culture. For western culture, this convention suggests an order from top to bottom
and left to right. The algorithm also uses conceptual structure to compute a depth-first
ordering of the hierarchy with overviews added for transitions between hierarchy levels
or groups. Naturally, this depth-first traversal could also be manually modified to use a
breadth-first or other tree traversal at any point in the hierarchy.
181
Figure 59: An example automatic layout with nested rectangular groups.
Figure 60: An example of an automatic layout with a network format.
182
An additional area for further work is navigation controls during the delivery of
ZUI presentation controls. Although unscripted navigation is one of the areas where ZUI
presentations can excel, the current interface controls still need refinement. A particular
problemwith CounterPoint?s navigation controls is that theydo not provide keyboard
shortcuts for zooming-in. In addition, presenters are likely to need more visual feedback
indicating the navigation options available to them.
6.2.2 Evaluation
This dissertation described an initial exploratory study looking at the risks and
benefits of ZUI presentations for the audience. Although this study provides some
evidence that spatial layouts are better at conveying information about presentation
structure, it did not find significant differences in the explicit measures of recall for
structure or content. As a result, additional studies are needed to verify this preliminary
evidence. Future studies could look at longer presentations, since ours was only 10
minutes, and could separate users based on their spatial and verbal cognitive abilities.
Future studies could also look at the impact of spatial layout and animation on the
audience for more specific kinds of presentations or presentations events. For example,
ZUI presentations seem to be able to communicate location in the presentation during
unexpected changes. A study could be designed to isolate this aspect of the presentation.
In addition, our initial study showed indications that animation may negatively
impact audience recall for content. This is likely to be a result of the novelty of these
animations. As these kinds of presentations become more common, this kind of study
could be repeated to see if this effect is mitigated by experience.
183
This dissertation reports a substantial amount of real world experience with
CounterPoint indicating its benefits for presentation delivery. However, controlled
studies are needed to formalize these benefits. Since we expect that CounterPoint may
help reduce the load on the presenter in communicating presentation structure and
location, future studies may want to investigate the amount of attention needed to deliver
these presentations as well as their resulting quality. Finally, formal studies are also need
to identify the best techniques for authoring ZUI presentations.
184
Chapter 7:
Conclusion
The goal of this work was to address the most prominent problems in using
current computer-based tools to author and deliver presentations. It is hard to
overestimate the costs incurred due to these problems with over 30 million PowerPoint
presentations being authored or delivered each day [82]. As software slide presentations
are becoming more common, presenters are becoming more discontented with flashy
visual effects, bulleted lists, chart junk, and auto-content wizards and are searching
instead for tools to help them effectively make sense of and present their large collections
of information.
For authoring tasks involving large collections of information, a widely
recognized problem in using computer software is the lack of sufficient overviews.
However, previous research has not clearly pinpointed the reasons why good overviews
are necessary for authoring. This dissertation identifies organization as a specific
component of authoring where effective overviews are essential. Good overviews are
necessary to reduce the time needed to compare objects, allowing the author to consider
more inter-object relationships in less time. The benefits of overviews for organization
were demonstrated in this dissertation through several software prototypes and user
studies.
In delivering presentations with large collections of information, presenters have
difficulty conveying the structure of their presentations to the audience. They also have
problems keeping the audience oriented in the presentation structure during impromptu
navigations through their presentation slides. CounterPoint, the zoomable presentation
185
tool described in this dissertation, introduces support for a new genre of presentations that
are hierarchically structured and support interactive navigation. This new genre of
presentations makes it possible to communicate larger and more complex presentation
structures while tailoring these presentations to the audience based on their feedback.
The benefits of these new presentations are demonstrated in this work through user
studies and real-world experiences.
7.1 Presentation Authoring
In comparing our early prototypes to existing authoring tools, small qualitative
studies found that users were predictably most effective with paper based authoring tools
on large physical surfaces. However, paper-based information is difficult to integrate
with normal computer-based work practices and the large physical surfaces are typically
not archivable or portable. Instead, this research explored techniques for a standard PC
that would allow the active computer display to compensate for the limited display space.
Nonetheless, the first qualitative studies of our prototype found that users spent too much
time navigating and managing space, which distracted them from the actual organization
task.
These disappointing results motivated a change in the design of Niagara to reduce
navigation costs by encouraging zooming. Three techniques were introduced to improve
the value of zoomed out objects including automatic text reduction, automatic grouping,
and dynamic text popups. Prior experience suggested that evaluating the revised version
of Niagara in a controlled setting would be difficult due to the cognitive complexity of
text-based authoring tasks. Instead, a simplified user study as designed to determine
whether ZUIs offer any advantages over more traditional techniques for organizing
186
information. Only when an effect was found in this simplified study was an evaluation
performed for text based tasks.
The organization task in our simplified study involved putting shapes into groups
based on common shape properties. The study compared a ZUI interface to a folder-
based interface for organizing the shapes. The results from 13 subjects indicated 30%
faster completion times and higher subjective ratings for the ZUI interface than for the
folder-based interface. These results seemed to be driven by the faster comparisons
between objects facilitated by the ZUI?s continuous workspace in contrast to the
disconnected folder workspaces.
Following on the promising results in the shape study, a similar controlled study
was designed to try to extend these results to text. A preliminary analysis of the data
from 14 subjects in this second study indicated a significant subjective preference for
ZUIs as well as fewer labeled groups in the ZUI condition. A closer look at the data
revealed 3 different labeling strategies that seemed to correlate with 3 different kinds of
users. Analyzing the data with labeling strategy as a between subjects factor showed a
significant effect of tool type on completion time and subjective preference and a
significant interaction of tool type and labeling strategy on completion time. All subjects
showed a substantial preference for the ZUI condition over the folder condition.
However, subjects were differentiated by labeling strategy in whether they were faster
with the ZUI or folders. Subjects who did not label their ZUI groups completed the task
faster with the ZUI condition whereas subjects who labeled all their groups completed the
task faster with the folder condition.
187
Overall, there were significantly fewer labeled groups in the ZUI condition versus
the folder condition. This difference suggests that subjects are generally able to keep
more of the groups, or at least the group criteria, in mind at once in the ZUI condition.
As before, one explanation for this phenomenon is that having all the objects on-screen at
once in the ZUI facilitates more rapid access to the objects and thus provides a more
accessible memory aid. An additional factor is that the groups in the ZUI workspace are
reinforced by spatial memory which is limited in the folder condition.
Although this research focused on organization tasks, many different kinds of
tasks are likely to benefit from the ZUIs support for holding more groups in mind at once.
One specific task domain where this may be particularly important is evidence structuring
and hypothesis management. A particular challenge in these tasks is maintaining
awareness of different kinds of evidence for several competing hypotheses. ZUIs may
facilitate keeping a larger number of hypotheses or different kinds of evidence in mind at
once than traditional techniques.
ZUIs had a more substantial impact in the shape organization task than in the text-
based organization task. One likely reason for this is that shapes retain more of their
value when zoomed out. This suggests, perhaps unsurprisingly, that the value of ZUIs
for an organization task is likely to depend directly on the quality of semantic zooming
representations available for the objects in the workspace. Nevertheless, these studies
demonstrated benefits for ZUIs over folders for organization tasks involving two
radically different object types. Consequently, there is reason to believe these results also
extend to other object types, such as web page thumbnails, pen input, images, etc.
188
7.2 Presentation Delivery
Our research on presentation delivery was largely driven by real-world
experiences using our ZUI presentation prototype, CounterPoint, to deliver actual
presentations. From this use, we developed intuitions leading to an evaluation of the
effects of ZUI presentations on audience preference and recall. In addition, this use also
revealed a number of presentation settings for which ZUI presentations are most
beneficial.
This dissertation describes a controlled study that compared presentations that
varied in their use of spatial layout and animation. The audience showed an overall
preference for presentations with spatial layout. They also rated spatial presentations
higher in terms of organization. Though not statistically significant, these subjective
ratings of organization were also supported by patterns in measures of audience recall for
outline structure and content. An additionalnon-significant pattern indicated that the
animated spatial layout presentations may result in lower recall for presentation details.
This last trend is likely due to novelty effects that will be alleviated as animated, spatial
presentations become more common.
This dissertation also compiled our own experiences and the experiences of 7
other presenters in using CounterPoint for well over 100 actual presentations. Presenters
cited the ability to communicate overviews, hierarchical structure, progress, and
orientation during presentation delivery as fundamental benefits of CounterPoint over
traditional software slide presentations. Experienced users also found compelling
CounterPoint?s facilities to gracefully navigate the presentation and dynamically tailor
189
their talk for the audience. A number of problems were also identified with our current
CounterPoint implementation that suggest areas for future work.
Through these formal and informal evaluations, we have found that ZUI
presentations are likely to be more important when the high level organization of the
presentation is correspondingly more important in the presenter's message. One situation
where this occurs is in highly dynamic "briefings" where audience feedback strongly
influences what slides and topics the presenter will cover. Communicating the structure
is also likely to be important in larger presentations with many topics and subtopics.
More generally, ZUI presentations are likely to be most effective in situations where a
substantial percentage of the presentation is spent in transitioning between different
levels of detail. Here, the ZUI?s spatial structure and animations are visible for a larger
percentage of the presentation allowing them to have a larger impact on the quality of the
presentation.
7.3 Contributions
This research on the authoring and delivery presentations contributes 6 major
innovations:
Authoring Presentations
? User Interface Techniques: Several interface techniques, including
automatic grouping and automatic text reduction, that facilitate interactions
with abstract information in 2D and ZUI environments.
? Implementation: An implemented system that combines automatic grouping
and automatic text reduction in a zoomable workspace.
190
? Empirical evaluations: Evaluations demonstrating the benefits of ZUIs over
a more traditional folder-based interface for tasks that involve organizing
information.
? Simplified Organization Task: A simplified organization task involving
shapes that can be reused in future research (e.g. in comparing large displays
to small displays)
Delivering Presentations
? System Implementation: A concrete ZUI presentation system that integrates
with an existing presentation tool and serves to guide future exploration in
more interactive, structured presentations.
? Empirical Evaluation: Evaluation demonstrating audience preference for
spatial presentations over traditional slide presentation.
? Real-World Validation: Positive feedback from presenters using
CounterPoint for a substantial number of actual presentations.
191
Appendix_A:
Questionnaires
A.1 Niagara Qualitative Study Questionnaire
Past Experience
1) What is your occupation?
2) How would you rate yourself in terms of computer experience?
(Circle one number)
Novice 12345Expert
3) What types of tasks do you use a computer for?
(Circle all that apply)
a. Word Processing
b. Games
c. Data analysis
d. Programming
e. Internet (Web/Email/News/Chat)
f. Design
g. Other____________________________
4) How often do you use PowerPoint?
a. Never
b. Rarely
c. Once a month
d. Once a week
e. At least once a day
5) Ifyou use PowerPoint, what types oftasks do you use it for?
(Circle all that apply)
a. Organizing information
b. Giving presentations
c. Creating drawings/figures
d. Making posters
e. Other____________________________
6) How do you usually do the type of task we?ve just given you?
Post-Its
7) What is your overall reaction to using Post-It notes for this task?
Frustrating 12345 Satisfying
8) How satisfied are you with your final organization of the driving facts?
Notsatisfied 12345 Satisfied
9) Do you think you could have done a better job with more time?
DefinitelyNot 12345 Definitely
10)How appropriate do you think the Post-It notes were for this task?
Notapropriate12345 Apropriate
11)What was your biggest frustration using Post-It Notes for this task?
12)What did you see as the biggest advantage of using Post-It Notes for this task?
PowerPoint
13)What is your overall reaction to using PowerPoint for this task?
192
Frustrating 12345 Satisfying
14)How satisfied are you with your final organization of the driving facts?
Notsatisfied 12345 Satisfied
15)Do you think you could have done a better job with more time?
DefinitelyNot 12345 Definitely
16)How appropriate do you think PowerPoint was for this task?
Notapropriate12345 Apropriate
17)What was your biggest frustration using PowerPoint for this task?
18)What did you see as the biggest advantage of using PowerPoint for this task?
Niagara
19)What is your overall reaction to using Niagara for this task?
Frustrating 12345 Satisfying
20)How satisfied are you with your final organization of the driving facts?
Notsatisfied 12345 Satisfied
21)Do you think you could have done a better job with more time?
DefinitelyNot 12345 Definitely
22)How appropriate do you think Niagara was for this task?
Notapropriate12345 Apropriate
23)What was your biggest frustration using Niagara for this task?
24)What did you see as the biggest advantage of using Niagara for this task?
A.2 Niagara Quantitative Shape Study Questionnaire
Background
1) What is your gender?
2) What is your age?
3) How would you rate yourself in terms of computer experience?
Novice 12345 Expert
4) How experienced are you using graphical authoring tools such as PowerPoint,
PhotoShop, etc??
Novice 12345 Expert
Zooming
1) What is your overall reaction to using this tool for the task?
Frustrating 12345Satisfying
2) How often did you feel like software interrupted your thinking?
Rarely 12345Often
3) What was your biggest frustration using this tool for the task?
4) What was the biggest advantage of using this tool for the task?
Folders
1) What is your overall reaction to using this tool for the task?
Frustrating 12345Satisfying
2) How often did you feel like software interrupted your thinking?
Rarely 12345Often
3) What was your biggest frustration using this tool for the task?
4) What was the biggest advantage of using this tool for the task?
193
A.3 Niagara Quantitative Text Study Questionnaire
Background
1) What is your age?
2) How would you rate yourself in terms of computer experience?
Novice 12345 Expert
Zooming
1) What is your overall reaction to using this tool for the task?
Frustrating 12345Satisfying
2) How often did you zoomin or out? When did you need to do this?
3) How often did you read the reduce text (not the popup text) while zoomed out?
4) What was your biggest frustration using this tool for the task?
5) What was the biggest advantage of using this tool for the task?
Folders
1) What is your overall reaction to using this tool for the task?
Frustrating 12345Satisfying
2) What was your biggest frustration using this tool for the task?
3) What was the biggest advantage of using this tool for the task?
A.4 CounterPoint Audience Questionnaire
Initial
1) How old are you?
2) What is your gender?
3) What is your major?
4) How many PowerPoint style presentations have you seen in the last year:
0lesthan1020morethan20
5) How familiar were you with genetic engineering prior to this presentation:
123456789
not familiar very familiar
6) Rate how much you liked the visual part of the presentation:
123456789
notatall verymuch
7) How would you rate the presentation?s organization:
123456789
poor excellent
8) Identifying the main areas covered in the presentation was:
123456789
difficult easy
9) Determining the current progress of the presentation was:
123456789
difficult easy
10)Please give an outline of the presentation including the presentation title, the main
areas covered, and any specific slide titles you can remember. Feel free to use
words and/or drawings to represent your outline.
11)List the two types of caterpillars mentioned in the presentation?
194
12)List the products of agricultural genetic engineering that the presentation
mentioned as available in US supermarkets now:
13)According to the presentation, what was the name of the corn that was removed
from the market after it was found in taco shells?
14)List one example of a situation that the presentation describes when a genetically
engineered product must be labeled:
15)List the differences mentioned in the presentation between traditional breeding
and genetic engineering:
16)List the general groups named in the presentation involved in direct or indirect
regulation of genetic engineering:
17)What were the main conclusions of the presentation?
18)Please write any other comments you have about the format (user interface) of the
presentation:
Follow-up
1) Please give an outline of the presentation including the presentation title, the main
areas covered, and any specific slide titles you can remember. Feel free to use
words and/or drawings to represent your outline.
2) What crop did the presentation say was the first genetically engineered whole
food on the market?
3) According to the presentation, what type of corn that was found to be harmful to
Swallowtail caterpillars?
4) List the government regulatory organizations mentioned in the presentation:
5) What characteristic did the presentation say was added to ?Golden Rice??
6) What were the main conclusions of the presentation?
195
References
1. Allen, B. ?Information Space Representation in Interactive Systems:
Relationship to Spatial Abilities.? In Proceedings of International Conference on
Digital Libraries, 1-10. New York: ACM Press, 1998.
2. Baddeley, A. D. and G. J. Hitch. ?Working Memory.? In The Psychology of
Learning and Motivation, edited by G. Bower, 8:47-90. New York: Academic
Press, 1974.
3. Baudisch,P.,N.Good,V.Bellotti,andP.Schraedley.?KeepingThingsin
Context: A Comparative Evaluation of Focus Plus Context Screens, Overviews,
and Zooming,? CHI 2002, ACM Conference on Human Factors in Computing
Systems, CHI Letters 4 (1): 259-266 (2002).
4. Baudisch, P., and R. Rosenholtz. ?Halo: A Technique for Visualizing Off-Screen
Locations,? CHI 2003, ACM Conference on Human Factors in Computing
Systems, CHI Letters 5 (1): 481-488 (2003).
5. Beard, D. V., and J. Q. Walker II. ?Navigational Techniques to Improve the
Display of Large Two Dimensional Spaces,? Behavior &Information
Technology 9 (6): 451-466 (1990).
6. Bederson, B. B. ?PhotoMesa: A Zoomable Image Browser Using Quantum
Treemaps and Bubblemaps,? UIST 2001, ACM Symposium on User Interface
Software and Technology, CHI Letters 3 (2): 71-80 (2001).
7. Bederson, B. B., and A. Boltman. ?Does Animation Help Users Build Mental
Maps ofSpatialInformation?? In Proceedings of Information Visualization
Symposium, 28-35. New York: IEEE Press, 1999.
8. Bederson, B. B., J. Grosjean, and J. Meyer. ?Toolkit Design for Interactive
Structured Graphics,? Technical Report CS-TR-4432, University of Maryland,
2003.
9. Bederson,B.B.,J.D.Hollan,K.Perlin,J.Meyer,D.Bacon,andG.W.Furnas.
?Pad++: A Zoomable Graphical Sketchpad for Exploring Alternate Interface
Physics,? Journal of Visual Languages and Computing 7: 3-31 (1996).
10. Bederson, B. B., J. Meyer, and L. Good. ?Jazz: An Extensible Zoomable User
Interface Graphics Toolkit in Java,? UIST 2000, ACM Symposium on User
Interface Software and Technology, CHI Letters 2 (2): 171-180 (2000).
11. Bell, B., and S. Feiner. ?Dynamic space management for user interfaces,? UIST
2000, ACM Symposium on User Interface Software and Technology, CHI Letters
2 (2): 239-248 (2000).
196
12. Blankenship, J., and D. Dansereau. ?The Effect of Animated Node-Link Displays
on Information Recall,? Journal of Experimental Education 68 (4): 293-308
(2000).
13. Boltman, A., and A. Druin. ?Children?s Storytelling Technologies,? Technical
Report CS-TR-4310, University of Maryland, 2001.
14. Bridwell-Bowles, L. S., P. Johnson, and S. Brehe. ?Computers and composing:
Case studies of experienced writers.? In Writing in Real Time: Modeling
Production Processes, edited by A. Matsuhashi, 81-107. Norwood: Ablex, 1987.
15. Bush,V.?AsWeMayThink,?The Atlantic Monthly 176 (1): 101-108 (1945).
16. Card,S.K.,T.P.Moran,andA.Newell.The Psychology of Human-Computer
Interaction. Hillsdale: Lawrence Erlbaum Associates, 1983.
17. Card, S. K., and D. Nation. ?Degree-of-Interest Trees: A Component of an
Attention-Reactive User Interface.? In Proceedings of AVI 2002.NewYork:
ACM Press, 2002.
18. Chang, B.-W., and D. Ungar. ?Animation: From Cartoons to the User Interface.?
In Proceedings of UIST '93, 45-55. New York: ACM Press, 1993.
19. Combs, T., and B. B. Bederson. ?Does Zooming Improve Image Browsing?? In
Proceedings of Digital Libraries '99, 130-137. New York: ACM Press, 1999.
20. Conklin, J., and M. Begeman. ?gIBIS: A hypertext tool for exploratory policy
discussion,? ACM Transaction on Office Information System 6 (4): 303-331
(1988).
21. Cormen, T., C. Leiserson, and R. Rivest. Introduction to Algorithms, Cambridge:
MIT Press, 1990.
22. Czerwinski, M., S. Dumais, G. Robertson, S. Dziadosz, S. Tiernan, and M. van
Dantzich. ?Visualizing implicit queries for information management and
retrieval,? In Proceedings of CHI ?99, 560-567. New York: ACM Press, 1999.
23. Czerwinski, M., E. Horvitz, and E. Cutrell. ?Subjective Duration Assessment: An
Implicit Probe for Software Usability.? In Proceedings of IHM-HCI 2001
Conference, edited by A. Blandford and J. Vanderdonckt, 2:167-170. Berlin:
Springer, 2001.
24. Dee-Lucas, D. ?Effects of Overview Structure on Study Strategies and Text
Representations for Instructional Hypertext.? In Hypertext and Cognition, edited
by J. Rouet, J. Levonen, A. Dillon, and R. J. Spiro, 73-106. Hillsdale: Lawrence
Erlbaum Associates, 1996.
197
25. Dertouzos, M. L. What Will Be: How the New World of Information Will Change
Our Lives. San Francisco: HarperEdge, San Francisco, 254.
26. Dieberger, A., and U. Frank. ?A City Metaphor to Support Navigation in
Complex Information Spaces,? Journal of Visual Languages and Computing 9:
597-622 (1998).
27. Dieberger A., C. Miner, and D. Ponceleon. ?Supporting narrative flow in
presentation software.? In Proceedings of CHI 2001 Extended Abstracts, 137-138.
New York: ACM Press, 2001.
28. Dillon, A., C. McKnight, and J. Richardson. ?Space - The Final Chapter or Why
Physical Representations Are Not Semantic Intentions.? In Hypertext ? A
Psychological Perspective, edited by C. McKnight, A. Dillon, and J. Richardson,
169-191. New York: Ellis Horwood, 1993.
29. Donskoy, M., and V. Kaptelinin. ?Window Navigation With and Without
Animation: A Comparison of Scroll Bars, Zoom, and Fisheye View.? In
Proceedings of CHI ?97 Extended Abstracts, 279-280. New York: ACM Press,
1997.
30. Druin,A.,J.Stewart,D.Proft,B.B.Bederson,andJ.D.Hollan.?KidPad:A
Design Collaboration Between Children, Technologists, and Educators.? In
Proceedings of CHI ?97, 463-470. New York: ACM Press, 1997.
31. Engelbart, D. C., and W. K. English. ?A research center for augmenting human
intellect.? In AFIPS Conference Proceedings, 33:395-410. Montvale: AFIPS
Press, 1968.
32. Flower, L. S. and J. R. Hayes. ?The Dynamics of Composing: Making Plans and
Juggling Constraints.? In Cognitive Processes in Writing,editedbyL.W.Gregg
and E.R. Steinberg, 31-50. Hillsdale: Lawrence Erlbaum Associates, 1980.
33. Foster, G. and M. Stefik. ?Cognoter: Theory and practice of a Colab-orative
tool.? In Proceedings of CSCW '86, 7-15. New York: ACM Press, 1986.
34. Freelance Graphics. IBM Corp., http://www.lotus.com/freelance, 2003.
35. Frenckner, K. ?The Problem of Getting a Global Perspective When Reading
Continuous Texts From a Computer Screen.? Technical Report TRITA-NA-
P9332, KTH Royal Institute of Technology, 1993.
36. Furnas, G. W., and B. B. Bederson. ?Space-Scale Diagrams: Understanding
Multiscale Interfaces.? In Proceedings of CHI ?95, 234-241. New York: ACM
Press, 1995.
37. Furnas,G.W.,andX.Zhang.?MuSE:AMultiscaleEditor.?InProceedings of
UIST ?98, 107-116. New York: ACM Press, 1998.
198
38. Furnas, G., and X. Zhange. ?Illusions of Infinity: Feedback for Infinite Worlds.?
UIST 2000, ACM Symposium on User Interface Software and Technology, CHI
Letters 2 (2): 237-238 (2000).
39. Ganzel,R.?Powerpointless.?
http://www.presentations.com/techno/soft/2000/02/29_f2_ppl.html, 2000
(Retrieved Sept. 14, 2003 via www.archive.org).
40. Gates, P. ?Where?s the Power? What?s the Point?? http://www.conference-
board.org/publications/atb/articles/gatesMay02_01.cfm, 2001 (Retrieved
September 14, 2003).
41. Ghosh, P., and B. Shneiderman. ?Zoom-only vs. overview-detail pair: A study in
browsing techniques as applied to patient histories.? Technical Report CS-TR-
4028, University of Maryland, 1999.
42. Good, L., and B. B. Bederson. ?Zoomable User Interfaces as a Medium for Slide
Show Presentations,? Information Visualization 1 (1): 35-49 (2002).
43. Good, L., B. B. Bederson, M. Stefik, and P. Baudisch. ?Automatic Text
Reduction for Changing Size Constraints.? In Proceedings of CHI 2002 Extended
Abstracts, 798-799. New York: ACM Press, 2002.
44. Guimbretire, F., M. Stone, and T. Winograd. ?Fluid Interaction with High-
resolution Wall-size Displays,? UIST 2001, ACM Symposium on User Interface
Software and Technology, CHI Letters 3 (2): 21-30 (2001).
45. Haake, J., C. Neuwirth, and N. Streitz. ?Coexistence and transformation of
informal and formal structures: Requirements for more flexible hypermedia
systems.? In Proceedings of ACM ECHT ?94, 1-12. New York: ACM Press, 1994.
46. Halasz, F. G. ?Reflections on NoteCards: Seven issues for the next generation of
hypermedia systems,? Comm. ACM 31 (7): 836-852 (1988).
47. Hancom Presenter. HancomLinux Inc.,
http://en.hancom.com/products/hancompresenter.html, 2003.
48. Harvard Graphics. Serif Inc., http://www.harvardgraphics.com, 2003.
49. Hayes, J. R. and L. S. Flower. ?Identifying the organization of writing
processes.? In Cognitive processes in writing,editedbyL.W.GreggandE.R.
Steinberg, 3-30. Hillsdale: Lawrence Erlbaum Associates, 1980.
50. Hays, T. A. ?Spatial Abilities and the Effects of Computer Animation on Short-
Term and Long-Term Comprehension,? Journal of Educational Computing
Research 14 (2): 139-155 (1996).
199
51. Henderson, D. A., and S. Card. ?Rooms: the use of multiple virtual workspaces
to reduce space conten-tion in a window-based graphical user interface,? ACM
Transactions on Graphics 5 (3): 211-243 (1986).
52. Hightower, R. R., L. Ring, J. Helfman, B. B. Bederson, and J. D. Hollan.
?Graphical Multiscale Web Histories: A Study of PadPrints.? In Proceedings of
Hypertext ?98, 58-65. New York: ACM Press, 1998.
53. Hinton, P. Statistics Explained: A Guide For Social Science Students.London:
Routledge, 1996.
54. Hornb?k, K., B. B. Bederson, and C. Plaisant. ?Navigation Patterns and
Usability of Zoomable User Interfaces with and without an Overview,? ACM
Transactions on Computer-Human Interaction 9 (4): 362-389 (2002).
55. Hunter, W. J. and J. Begoray. ?A framework for the activities involved in the
writing process,? The Writing Notebook 8 (1), (1990).
56. Igarashi, T., and K. Hinckley. ?Speed-dependent automatic zooming for
browsing large documents,? UIST 2000, ACM Symposium on User Interface
Software and Technology, CHI Letters 2 (2): 139-148 (2000).
57. Impress. http://www.openoffice.org/product/impress.html, 2003.
58. Jaffe, G. ?Pentagon cracks down on ? PowerPoint.? The Wall Street Journal
Online, April 26, 2000.
59. Johnson, J. and B. Nardi. ?Creating presentation slides: A study of user
preferences for task-specific vs, generic application software,? ACM
Transactions on Computer-Human Interaction 3 (1): 38-65 (1996).
60. Jones, W. P. and S. T. Dumais. ?The Spatial Metaphor for User Interfaces:
Experimental Tests of Reference by Location versus Name.? ACM Transactions
on Office Information Systems 4 (1): 42-63 (1986).
61. Jul, S., and G. W. Furnas. ?Critical Zones in Desert Fog: Aids to Multiscale
Navigation.? In Proceedings of UIST ?98, 97-106. New York, ACM Press, 1998.
62. Kaplan, N., and S. Moulthrop. ?Where No Mind Has Gone Before: Ontological
Design for Virtual Spaces.? In Proceedings of ECHT ?94, 206-216. New York:
ACM Press, 1994.
63. Keynote. Apple Computer, Inc., http://www.apple.com/keynote/, 2003.
64. KPresenter. http://www.koffice.org/kpresenter/, 2003.
200
65. Lai, S. ?Increasing Associative Learning of Abstract Concepts Through
Audiovisual Redundancy,? Journal of Educational Computing Research 23 (3):
275-289 (2000).
66. Lambiotte, J., D. Dansereau, D. Cross, and S. Reynolds. ?Multirelational
Semantic Maps,? Educational Psychology Review 1 (4): 331-367 (1989).
67. Marcu, D. The Theory and Practice of Discourse parsing and Summarization.
Cambridge: MIT Press, 2000.
68. Marshall, C. C., and R. A. Rogers. ?Two Years before the Mist: Experiences
with Aquanet.? In Proceedings of Hypertext ?92, 53-62. New York: ACM Press,
1992.
69. Marshall, C. C., and F. M. Shipman. ?Spatial Hypertext and the Practice of
Information Triage.? In Proceedings of Hypertext '97, 124-133. New York: ACM
Hypertext, 1997.
70. Marshall,C.C.,F.M.Shipman,andJ.H.Coombs.?VIKI:SpatialHypertext
Supporting Emergent Structure.? In Proceedings of ACM ECHT '94, 13-23. New
York: ACM Press, 1994.
71. Miller, G. ?The Magical Number Seven, Plus or Minus Two: Some Limits on
Our Capacity for Processing Information,? Psychological Review 63: 81-97
(1956).
72. Monty, M. L. ?Issues for supporting notetaking and note using in the computer
environment.? Ph.D. Dissertation, Department of Psychology, University of
California, San Diego, 1990.
73. Moore, T. ?Active Use of Hypertext to Aid Learning and Classroom Instruction.?
In Proceedings of Technical Symposium on Computer Science Education, 297-
301. New York: ACM Press, 1995.
74. Mynatt, E., T. Igarashi, W. Edwards, and A. LaMarca. ?Flatland: New
Dimensions in Office Whiteboards.? In Proceedings of CHI '99, 346-353. New
York: ACM Press, 1999.
75. Nelson, L., S. Ichimura, E. Pedersen, and L. Adams. ?Palette: A Paper Interface
for Giving Presentations.? In Proceedings of CHI ?99, 354-361. New York, ACM
Press, 1999.
76. Neuwirth, C., D. Kaufer, R. Chimera, and T. Gillespie. ?The Notes program: A
hypertext application for writing from source texts.? In Proceedings of
Hypertext ?87, 121-142. New York: ACM Press, 1987.
77. Norman,D.A.The Psychology of Everyday Things. New York: BasicBooks,
1988.
201
78. Norman, K. The Psychology of Menu Selection: Designing Cognitive Control at
the Human/Computer Interface. Norwood: Ablex, 1991.
79. OneNote. Microsoft Corp., http://www.microsoft.com/onenote/, 2003.
80. Paez, L.B., J. B. daSilva, and G. Marchionini. ?Disorientation in electronic
environments: A study of hypertext and continuous zooming interfaces.? In
Proceedings of the Asis Annual Meeting 33, 58-66. Silver Spring: ASIS, 1996.
81. Paivio, A. Mental Representation: A Dual Coding Approach.NewYork:Oxford
University Press, 1986.
82. Parker, I. ?Absolute PowerPoint: Can a software package edit our thoughts.?
New Yorker, May 28, 2001, 76-87.
83. Patterson, M., D. Dansereau, and D. Wiegmann. ?Receiving Information During
a Cooperative Episode: Effects of Communication Aids and Verbal Ability.?
Learning and Individual Differences 5 (1): 1-11 (1993).
84. Pederson, E., K. McCall, T. Moran, and F. Halasz. ?Tivoli: An Electronic
Whiteboard for InformalWorkgroup Meetings.? In Proceedings of InterCHI '93,
391-398. New York: ACM Press, 1993.
85. Perlin, K., and D. Fox. ?Pad: An alternative approach to the computer interface.?
In Proceedings of SIGGRAPH '93, 57-64. New York: ACM Press, 1993.
86. Perlin, K., and J. Meyer. ?Nested User Interface Components,? UIST 99, ACM
Symposium on User Interface Software and Technology, CHI Letters 1(1) 11-18
(1999).
87. Persuasion. Adobe Systems, Inc., http://www.adobe.com/prodindex/persuasion/,
2002.
88. Plumlee, M., and C. Ware. ?Zooming, Multiple Windows, and Visual Working
Memory.? In Proceedings of AVI 2002, 59-68. New York: ACM Press, 2002.
89. PowerPoint. Microsoft Corp., http://www.microsoft.com/powerpoint/, 2003.
90. Prante, T., C. Magerkurth, and N. A. Streitz. ?Developing CSCW tools for Idea
Finding - Empirical Results and Implications for Design,? CSCW 2002, ACM
Conference on Computer Supported Collaborative Work, CHI Letters 4 (3): 106-
115 (2002).
91. Presentations. Corel Corp., http://www.corel.com, 2003.
92. Rivlin, E., R. Botafogo, and B. Shneiderman. ?Navigating in Hyperspace:
Designing a Structure-Based Toolbox.? Comm. ACM 37 (2): 87-96 (1994).
202
93. Robertson, G., M. Czerwinski, K. Larson, D. C. Robbins, D. Thiel, and M. van
Dantzich. ?Data Mountain: using spatial memory for document management,? In
Proceedings of UIST '98, 153-62. New York: ACM Press, 1998.
94. Robinson, D., A. Katayama, and A. Fan. ?Evidence for Conjoint Retention of
Information Encoded From Spatial Adjunct Displays,? Contemporary
Educational Psychology 21: 221-239 (1996).
95. Robinson, D., S. Robinson, and A. Katayama. ?When Words Are Represented in
Memory Like Pictures: Evidence for Spatial Encoding of Study Materials.?
Contemporary Educational Psychology 24: 38-54 (1999).
96. Samet, H. The Design and Analysis of Spatial Data Structures. Reading:
Addison-Wesley, 1990.
97. Severinson Eklundh, K. ?Problems in achieving a global perspective of the text
in computer-based writing,? Instructional Science 21: 73-84 (1992).
98. Sharples, M. ?An account of writing as creative design.? Chap. 8 in The Science
of Writing: Theories, Methods, Individual Differences and Applications, edited
by C. M. Levy and S. Ransdell. Hillsdale: Lawrence Erlbaum Associates, 1996.
99. Shipman, F., R. Furuta, and C. Marshall. ?Generating Web-Based Presentations
in Spatial Hypertext.? In Proceedings of IUI ?97, 71-78. New York: ACM Press,
1997.
100. Shipman, F., H. Hsieh, R. Airhart, P. Maloor, and J. M. Moore. ?The Visual
Knowledge Builder: A Second Generation Spatial Hypertext.? In Proceedings of
Hypertext 2001, 113-122. New York: ACM Press, 2001.
101. Shipman, F., and C. C. Marshall. ?Formality Considered Harmful: Experiences,
Emerging Themes, and Directions on the Use of Formal Representations in
Interactive Systems,? Computer Supported Cooperative Work 8 (4): 333-352
(1999).
102. Shipman, F.M., C. C. Marshall, and M. LeMere. ?Beyond Location: Hypertext
Workspaces and Non-Linear Views.? In Proceedings of Hypertext ?99, 121-130.
New York: ACM Press, 1999.
103. Shipman, F.M., C. C. Marshall, and T. P. Moran. ?Finding and Using Implicit
Structure in Human-Organized Spatial Layouts of Information.? In Proceedings
of CHI '95, 346-353. New York: ACM Press, 1995.
104. Shneiderman, B. ?Creating Creativity: User Interfaces for Supporting
Innovation,? ACM Transactions on Computer-Human Interaction 7 (1): 114-138
(2000).
203
105. Shneiderman B. Designing the User Interface. 3rd ed. Reading: Addison-Wesley,
1998.
106. Shneiderman, B. ?The Limits of Speech Recognition.? Comm. ACM 43 (9): 63-
65 (2000).
107. StorySpace. Eastgate Systems, Inc., http://www.eastgate.com/storyspace/, 2003.
108. Sutherland, I. ?SketchPad: A man-machine graphical communication system.? In
AFIPS Conference Proceedings 23, 323-328. Montvale: AFIPS Press, 1963.
109. Th?ring, M., J. Hannemann, and J. Haake. ?Hypermedia and Cognition:
Designing for Comprehension.? Comm. ACM 38 (8): 57-66 (1995).
110. Tinderbox. Eastgate Systems, Inc., http://www.eastgate.com/Tinderbox/, (2003).
111. Toyoda, M., and E. Shibayama. ?Hyper Mochi Sheet: A Predictive Focusing
Interface for Navigating and Editing Nested Networks Through a Multi-Focus
Distortion-Oriented View.? In Proceedings of CHI ?99, 504-511. New York:
ACM Press, 1999.
112. Trigg, R. ?Guided Tours and Tabletops: Tools for Communicating in a Hypertext
Environment,? ACM Transactions on Office Information Systems 6 (4): 398-414
(1988).
113. Tufte, E. R. The Cognitive Style of PowerPoint. Chesire: Graphics Press LLC,
2003, 1-24.
114. Wardrip-Fruin, N., J. Meyer, K. Perlin, B. B. Bederson, and J. D. Hollan. ?A
Zooming Sketchpad, A Multiscale Narrative: Gray Matters.? In Visual
Proceedings of SIGGRAPH '97, 141. New York: ACM Press, 1997.
115. Web Squirrel. Eastgate Systems, Inc., http://www.eastgate.com/squirrel/, 2003.
116. Wiegman, D., D. Dansereau, E. McCagg, K. Rewey, and U. Pitre. ?Effects of
Knowledge Map Characteristics on Information Processing,? Contemporary
Educational Psychology 17: 136-155 (1992).
117. Woodruff, A., A. Faulring, R. Rosenholtz, J. Morrison, and P. Pirolli. ?Using
Thumbnails to Search the Web,? CHI 2001, ACM Conference on Human
Factors in Computing Systems, CHI Letters 3 (1), 198-205 (2001).
118. Zaphiris, P., and L. Mtei,. ?Depth vs. Breadth in the Arrangement of Web
Links.? http://otal.umd.edu/SHORE/bs04, 1997.
119. Zellweger, P. Active Paths Through Multimedia Documents. In Proceedings of
International Conference on Electronic Publishing, Document Manipulation and
Typography, 19-34. New York: ACM Press, 1988.
204
120. Zellweger, P. ?A Hypermedia Path Mechanism.? In Proceedings of Hypertext
?89, 1-14. New York: ACM Press, 1989.
121. Zellweger, P., J. Mackinlay, L. Good, M. Stefik, and P. Baudisch. ?City Lights:
Contextual Views in Minimal Space.? In Proceedings of CHI 2003 Extended
Abstracts, 838-839. New York: ACM Press, 2003.