Published in the Proceedings of CHI 2002, April 20-25 2001, ACM Press, ©2002 ACM
A Tangible Interface
for Organizing Information Using a Grid
Robert J.K. Jacob*, Hiroshi Ishii, Gian Pangaro, and James Patten
Tangible Media Group, MIT Media Laboratory , Cambridge, Mass. USA
{rjacob, ishii, pangaro, jpatten}@media.mit.edu
ABSTRACT
The task of organizing information is typically performed
either by physically manipulating note cards or sticky notes
or by arranging icons on a computer with a graphical user
interface. We present a new tangible interface platform for
manipulating discrete pieces of abstract information, which
attempts to combine the benefits of each of these two
alternatives into a single system. We developed interaction
techniques and an example application for organizing
conference papers. We assessed the effectiveness of our
system by experimentally comparing it to both graphical
and paper interfaces. The results suggest that our tangible
interface can provide a more effective means of organizing,
grouping, and manipulating data than either physical
operations or graphical computer interaction alone.
notecards on a desk or, by collaboratively arranging sticky
notes on a board. Such arrangement often begins in a freeform
way, by accreting small groups of related items, and,
later, develops into a larger structure or framework. Tasks
like this have thus far been surprisingly resistant to
computer support, perhaps because notecards or sticky
notes allow manipulation that is more natural and fluid and
particularly, free form without a predefined framework.
Even when the information to be organized already exists
in electronic form and the final output must be produced in
digital form, many people find it advantageous to copy the
information onto pieces of paper, manipulate them
manually, and then re-enter the resulting organization into
the computer.
Keywords
Tangible user interfaces, physical interaction, computersupported
cooperative work, sensing board, conference
planning, scheduling, direct manipulation, experiment.
INTRODUCTION
Tangible user interfaces (TUI) have been most compelling
in spatial or geometric application domains such as urban
planning[15], where the physical arrangement of the
objects to be manipulated has an obvious, inherent meaning
in the application. We want to explore the use of tangible
user interfaces in a wider range of more abstract
information tasks, where they have been less fully
developed. We have developed a new platform and
tangible user interface for manipulating, organizing, and
grouping pieces of information, which we believe to be
especially suited to tasks involving discrete data, abstract
or non-geometric data, and collaborative group work. We
present our complete system, Senseboard, and an
application built with it (see Figure 1). We then use it in a
more abstract, simplified form in an experiment to evaluate
the performance of our tangible interface.
The basic task our system addresses is common to many
application areas: manipulating a set of information items
or nodes. People often perform this task by arranging paper
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CHI 2002, April 20-25, 2002, Minneapolis, Minnesota, USA.
Copyright 2001 ACM 1-58113-453-3/02/0004…$5.00.
Figure 1. The Senseboard in use.
By providing a tangible user interface for this task, we seek
to blend some of the benefits of manual interaction with
those of computer augmentation. We expect the basic
manual physical approach to give us: (1) a natural, freeform
way to perform organizing and grouping; (2) rapid,
fluid, two-handed manipulation, including the ability to
grab and move a handful of items at once (in contrast to
mouse interaction); and (3) a platform that easily extends to
collaboration (unlike the conventional mouse and keyboard
interface). In addition to these features, our system adds
functionality that only the computer can provide, such as:
(1) selectively revealing details about each item; (2)
displaying alternate views of the data; (3) sorting and
___________________________
*and Department of Electrical Engineering and Computer Science, Tufts
University, Medford, Mass. USAsearching; (4) saving and restoring alternative
arrangements of the data; (5) exporting the arrangement to
some final output medium (e.g., schedule, shelf list, web
pages); and (6) allowing easy backup of an arrangement,
which is not possible with a set of physical sticky notes on
a board in a meeting room. Current approaches give one or
the other of these two sets of advantages. We want to
provide a point in the design space between physical and
digital, which blends some of the benefits of each in a
single platform.
We also present an experimental study that compares our
tangible interface to both a purely physical and a purely
graphical counterpart and suggests better performance with
the tangible interface than either of the two alternatives.
We then investigate this issue further, since we expect that
a tangible interface will contain selective benefits of
physical and digital interfaces rather than all the benefits of
both. We believe our interface preserves some of the good
qualities of physical paper, but not all, because the TUI is
inevitably an imperfect simulation of the real world. We
attempt to quantify this tradeoff by comparing to a tangible
interface with all benefits of computer enhancement
removed. This allows us to measure the performance
penalty paid for the imperfect simulation inherent in the
TUI separately from the benefit of the computer
enhancements; our goal is to design a system in which the
latter outweighs the former.
TANGIBLE USER INTERFACES
Tangible user interfaces are a growing area of user
interface research that use physical forms to represent data.
They were so named by Ishii and Ullmer[3], with roots in
seminal work of Wellner[9], and Fitzmaurice[1], and
continue a growing range of more recent work[2, 7, 8, 10,
11, 14, 15]. Instead of a generic screen, mouse, and
keyboard capable of representing all types of data, TUI
uses specific physical forms to represent and manipulate
the pieces of data in the system. TUI often uses simple,
transparent mechanical structures, so the user can use his or
her existing knowledge of the physical world to figure out
how to operate them and what they mean. TUI also often
involves the augmentation of existing physical objects by
adding digital meaning to the objects and their
manipulation[11, 16]. Some information and state is thus
represented directly by the physical objects, while
additional information is provided digitally, typically by
video projection onto the physical objects (e.g., a digital
shadow of a physical building model). We attempt to
combine the physical and digital representations to exploit
the advantages of each.
Tangible user interfaces can also be viewed as the further
evolution of GUI or direct manipulation style interaction.
The essence of a GUI is that the user seems to operate
directly on the objects in the computer rather than carrying
on a dialogue about them[12]. However, while the objects
are depicted realistically on the GUI display, the input and
output devices themselves are generic and malleable,
typically a mouse and a raster display. The price paid is
that their physical form gives no useful information to the
user about the objects, the task, the possible commands, or
the state of the system. TUI increases the realism of the
objects by allowing the user to interact even more directly
with them.
A concept underlying these interaction styles is to build on
the “natural” equipment and skills humans have acquired
through evolution and experience and exploit these for
communicating with the computer. For example, we posit
that grabbing and moving real objects directly is a more
natural skill than pointing or referring to them indirectly.
Direct manipulation moved user interfaces in this direction;
TUI pushes further toward such “natural” interaction.
However, an interface typically will rarely be entirely
“natural” or physical (or else it would not require a
computer at all); instead the design goal is to try to do as
much as possible with natural interactions, before adding
“artificial” ones[13].
APPLICATION DOMAIN
We want to suggest a plausible future tangible user
interface for performing a realistic knowledge worker or
office task. The basic application we consider is the generic
task of arranging, organizing, or grouping data objects.
These data items might be messages, files, bookmarks,
citations, papers for a literature search, slides for a
presentation, scenes for a movie, newspaper stories, pages
for a web site, employees to be reorganized, MP3 files to
be played by a disc jockey, ideas from a brainstorming
session, or papers to be presented at a conference.
For specificity, we focus here on a task familiar to some
readers: the job of organizing conference papers into
sessions and scheduling them. It shares most of the key
properties of other more general information manipulation
tasks, and we will use it as a stand-in for this general class
of tasks, without loss of generality. The conference paper
task we address begins after the papers have been accepted
or rejected, and involves two phases. First, it requires the
accepted papers to be grouped into sessions of three papers
each, related to each other by topic. Since the papers are
accepted or rejected independently, without regard to
whether they will fit into groups of three, the resulting
papers do not fall neatly into such groups, and this task
often requires some false starts and tradeoffs to fit them
into groups. The second phase of the task requires that the
sessions be assigned to time slots. In our example, there are
two or three sessions at the same time, so sessions
occurring in parallel should not overlap each other in topic
areas nor in the speakers who must be present for the
sessions.
The first part of the task thus provides an example of an
unstructured organizing or grouping task (building up
small groups by accretion), while the second one is a more
structured task (assigning items to predefined slots).
Together, they cover the important features of the wider
range of example applications mentioned above. Though
we draw on one of the authors' (Jacob) experience as
Papers Co-chair of the ACM CHI 2001 conference, notethat this system was not actually used to produce the CHI
2001 papers program. In fact, the task was performed for
CHI 2001, as in other years and in many other conferences,
using manual, “tangible” interaction. For the first phase, we
printed out each of the 69 accepted papers (which had
originally been submitted electronically) and spread them
out on the floor. Committee members worked together to
move the papers around on the floor, accumulating small
groupings of related papers, exchanging papers to improve
groupings, and gradually converging on 23 groups of three
each. For the second phase, we made a single index card
for each group of three papers (rather awkwardly, by
printing the list of accepted papers, cutting the printout into
little strips, and pasting the appropriate strips onto cards).
We then stuck the cards onto a large schedule grid drawn
onto a whiteboard and examined and rearranged them
several times. When the arrangement was finalized, we
manually transcribed the data from the schedule board onto
a computer. Both phases were performed by groups of
people (a subset of the Associate Papers Chairs) working
collaboratively.
Despite having excellent computer support at this meeting
(held in a computer-enhanced meeting room at Microsoft
Research), having all the relevant data already in electronic
form, and needing to produce our final results in electronic
form, the committee found it more effective to use
manual/tangible interaction to perform this task this year,
as in previous years. All of the input data were “born
digital.” The CHI papers were submitted electronically as
PDF files, along with a web-based form for entering title,
author, and other information; and the final destination was
a digital database from which the CHI hardcopy advance
program and web site were generated. Our system shows
an example of how the benefits of physical interaction can
be extended to a computer system.
Figure 2. The Senseboard device.
DESIGN
Senseboard consists of a vertical panel 1.1 m. wide x 0.8
m. high, mounted rather like a portable whiteboard, as seen
in Figure 2. It is marked with a rectangular grid of 12
columns x 20 rows, covering most of its surface, giving it
the appearance of a spreadsheet layout. Small rectangular
plastic tags or “pucks,” resembling white refrigerator
magnets, can be placed into these cells and stick there
magnetically. Each puck is 5.8 cm. wide x 2.5 cm. high
and is magnetized so that it can only be placed squarely
into a grid cell. It contains a small RFID (radio frequency
identification) tag, which is a passive device polled by a
radio frequency signal transmitted by the board beneath it.
Each time the user moves a puck, the board sends the
identity and the grid location of each of the pucks in the
grid to a computer on a serial port. The board is a
component of a Bannou Pro Intelligent White Board™
(Uchida Yoko Ltd., Japan). The pucks are our own
design, based on those of the Bannou Pro device, but
modified so that the user can send additional commands to
the system by pressing the surface of the puck or by briefly
placing one of several special command pucks over the
puck. We coupled this system to a computer (Dell Pentium
II PC) driving a video projector, which projects general
information onto the board and specific information onto
each puck. Our software that generates the projected data
is written as a Java application running on Windows 98,
receiving input from the board over a serial port and
sending its output to the video projector.
Our system combines projection with physical
manipulation in ways similar to the early work of
Wellner[9] and Fitzmaurice[1] and more recent work such
as DataTiles[11], metaDESK[14], and Sensetable[10].
Unlike several previous devices that used computer vision,
the Senseboard uses RFID sensing, which provides greater
reliability and speed. Other related projects include
LiveBoard[6], LegoWall (described in [2]), and Outpost[5].
Our device is also unusual in that it supports discrete, semistructured
interaction, since the pucks can only be placed
directly in grid cells. It is best used for tasks where
completely free-form input would not be appropriate—
where the physical constraint of placing pucks directly into
grid cells mirrors the constraints of the task itself and thus
provides a useful affordance for the task. Scheduling with
respect to a grid of discrete time slots is one such task. In
our application, the available time slots and the number of
papers that can be assigned to each are represented
naturally and implicitly by the arrangement of the grid cells
and the fact that only one puck can be physically inserted
into each grid cell. Finally, because the pucks are held to
the board by the grid of magnets, the board can be angled
vertically, rather than horizontally, allowing easier use by a
larger group of people working together. In sum, the use of
unique RFID tags, multiple pucks operating on the same
platform, a vertical work surface, and a constrained grid
layout together form a new tangible interface platform,
which we use to explore novel interaction techniques.
Rationale
Our overall goal is to make the data items we present
tangible and graspable; the user should feel like he or she is
touching the real data, as if they were “tangible bits.” Our
interface design decisions are guided by an attempt to make
each puck seem like a real computer data object, ratherthan like a handle that is controlling remotely-located
displayed data. Our platform accommodates plenty of
pucks, and we normally provide a separate puck for each
data item, permanently attached to it, although exceptions
are permitted for some operations. Interacting using a
mouse could be viewed as the opposite extreme, in that
there is only one physical “puck,” and it can be attached to
only one data object at a time, usually for very brief
periods. Bricks[1] provides an intermediate point. We also
exploit physical representation, with specially shaped
physical objects to depict commands, and use purely digital
representation where appropriate, such as continuous
display of the implications of the user's physical actions.
Data Objects
In the basic interface, each data object (a conference paper)
is projected onto a cell with a puck. We begin with a design
intentionally devoid of specific interaction techniques: the
user simply interacts “naturally” with the objects, by seeing
them, grabbing, them, and moving them. To organize the
papers, the user need only move them around on the grid to
form any desired arrangement or grouping. In our sample
task each object is a full paper accepted for the CHI 2001
conference. A magnetic puck represents each item with
some information about the paper (number, title, author)
projected onto its smooth white surface. If the user moves a
puck to a new cell, its data moves with it. Figure 1 shows
the board in use, with each paper represented by a puck on
the lower region of the board. The users have moved the
papers around the board, and are beginning to arrange them
by topic similarity. Here, the board grid is divided into two
regions, one for free-form topic grouping at the bottom and
one representing the time schedule of the conference at the
top.
Figure 3. Using the “View Details” command puck to see more
information about one of the data items by placing it over that
item. Note how the details are projected onto the larger
projectable surface of the command puck, temporarily obscuring
several adjacent pucks.
Commands
Pucks represent operands or nouns in a very
straightforward way. We add operators or verbs by
embodying each command in a special puck, shaped
differently from the others, to be thought of as a tool or
operator or stamper, which can be momentarily placed over
a regular data puck to operate on it like a rubber stamp.
These pucks are noticeably thicker than the regular pucks,
with distinctive shapes, suggesting the specific function of
each tool. Thus flat pucks represent data, and tall, specially
shaped pucks represent tools; the tools are placed over the
data pucks.
The “View Details” command puck has a larger projectable
surface than the data pucks. It allows the user to view
additional information about a given item, temporarily
overcoming the limitation of the small size of the data
pucks. We display information that would not normally fit
onto a puck by temporarily obscuring the adjacent pucks.
The “View Details” command puck is placed over the puck
to be viewed, and the information is displayed until the
command puck is removed. The detail information is
projected onto the larger command puck area, and the fact
that it physically obscures the adjacent cells below it
suggests to the user, in a direct physical way, that this
temporary information is being placed over those cells, and
that the cells below it are still present, but temporarily
obscured (see Figure 3).
Figure 4. “Group” command: The user is about to execute the
command by placing its command puck (with the arrow) over the
topmost data item of the column to be grouped. The items will
then be grouped into a single item, at the location of the command
puck.
The “Group” command allows the user to take several
items and combine them into a new, single item,
representing the group. For the CHI papers task, this is like
grouping (usually three) papers into a single conference
session. You first arrange the items on the board to form
small groups of interest, placing the items you wish to
group near each other on the board, in a single contiguous
column. You then apply the Group command by placing
the command puck over the first of the items to be grouped,
rather like first arranging papers together and then stapling
them. The arrow shape of this command puck suggests its
function of swallowing the pucks below into a new group
(see Figure 4). An “Ungroup” command operates
analogously and explodes a displayed group item back into
its component items. Its command puck is a down arrow,
suggesting it will explode the current item into cells below.
To show how a user might enter new data from a keyboard,
we provide a “Type-in” command. The typed text appears
at the bottom right of the board, outside of the cell grid.
You then place the corresponding command puck on a
blank cell and a new item appears there containing the
newly-entered data.The “Copy” or “Link” command displays the original data
item and the copy, with a line drawn between them; each
can be manipulated with its own puck. This demonstrates
how a user might create a line or graph edge rather a new
item. The command is intended to allow a user to explore
alternative organizations of the data, where one item might
need to appear in two places at once. You place the
command puck on the original item and then on a blank
puck where you want the copy.
In addition to the information projected onto individual
pucks, we display general information on the board. This is
where computer augmentation provides a clear advantage
over sticky notes. When the user places a puck representing
a paper into a time slot on the schedule grid, any resulting
conflicts are shown graphically. For example, when papers
on similar topics are presented in parallel, color-coded lines
are drawn between conflicting papers. This information is
updated continuously, so the user can see the implications
of choices as he or she makes them (Figure 5). Perhaps the
most obvious advantage of computer augmentation is the
“Export” command, which allows users to convert the final
arrangement of the items into a digital form that can be
exported to another program, avoiding the need to
transcribe the results after arranging sticky notes on a
board.
Figure 5. Displaying statistics and conflict information: As the
user places papers into the slots of the time schedule in the upper
area of the board, the system displays conflicts between papers
scheduled for the same time and involving the same author or
similar topics. Lines represent conflicts and overall statistics are
shown at the right edge of the board.
Alternative Designs
We also implemented a second approach for representing
commands. Instead of a separate puck for each command,
we display the commands in a reserved area of the board
grid, along the bottom. To apply a command to a puck, you
bring the puck to the cell that represents that command. For
example, the View details command is a single cell at the
bottom left of the board. When you place a puck there, its
information is shown, just below it, outside of the grid. The
Group command area has cells for several member items
plus one for a newly created group item. You place the
pucks for the objects to be grouped into the member item
area, and then place a new puck in the group item area, and
a new group item appears there, attached to the new puck.
This approach seems conceptually simpler, and somewhat
more visible to the user, but we generally prefer the
command puck alternative, because it allows the user to
operate on data items without moving them from their
current locations. It also exploits the physicality of the
command puck to communicate its function.
We also provide a third approach, in which the user simply
presses on the surface of a puck to execute a command on
it. This is convenient, but more limited, because it can
handle only one command, much like the GUI doubleclick.
It can coexist with the others, just as GUIs use
double-click for one command, combined with other means
for executing the remaining commands. We use this as an
alternative View details command.
The overall design begins to define an interaction language
for using pucks on a grid. Its syntactic elements include
thin pucks for nouns and thick ones for verbs, stamping
one puck over another, contiguous groups of pucks,
pressable pucks, and representing commands by special
puck shapes or reserved spatial locations. Our system
shows these for the conference papers task as a concrete
example of a broad class of information organizing tasks.
The data items could equally well be newspaper stories,
movie scenes, music clips, paper citations, or web pages.
EXPERIMENT
We conducted an experiment to attempt to quantify the
costs and benefits possible from tangible interaction,
compared to graphical and to purely physical interaction.
Because the TUI provides natural interaction with real
physical objects with their physical affordances and
constraints, we expect TUI to provide some benefit
compared to GUI. Moving the objects around with your
hands can help you think about a problem, because the
objects and their configuration embody and retain
information about the task state and constraints directly[4].
However, we expect that our TUI cannot match all the
benefits of either physical objects or GUI perfectly, but it
can provide an otherwise unobtainable blend of benefits of
both and, possibly, a performance improvement over each.
We also expect TUI to suffer a penalty, compared to pure
physical interaction arising from the imperfections in the
way that the tangible interface simulates the physical
world. However, the TUI can provide additional benefits
not possible with plain sticky notes, such as displaying
computed information as the user interacts with the objects,
so we then expect to gain a performance improvement from
such enhancements. The design goal is that, overall, these
additional benefits outweigh the penalties paid in going
from paper to TUI; our experiment attempts to measure
these two components separately.
Task
For this experiment, we simplified the previous application
considerably. Since subjects would not be familiar with the
intricacies of CHI paper scheduling, we needed a simple,
self-contained task that could be learned and performed in
a single experimental session. Ours required subjects to
plan a working schedule for a group of employees within a
set of constraints. We sought to emulate, from the subject's
point of view, a more difficult, open-ended, real-world task
(such as the CHI papers application), where the rules mightnot all be given explicitly, and the task would require more
judgment. Our task was designed to exercise similar skills
on the part of the subject, but in a more artificial and
simplified way for a closed-end experiment. In pilot
testing, we determined that the task was sufficiently easy
and self-contained and that most subjects could learn it and
complete it correctly fairly quickly. Accuracy was high
enough across all conditions that speed, rather than quality,
could provide a unified measure of performance.
The subject's task was to create a work schedule. Each of 5
days required 3 workers from a pool of 6 and had to meet a
set of constraints, e.g., workers had different skills and
each day required a mix of skills, some workers could not
work on consecutive days. The schedule was presented as a
grid on the board (5 days x 3 workers per day); workers
were represented by pucks. The subject could assign a
worker to a slot in the schedule by placing a puck into a
schedule slot, and rearrange them as needed to meet the
constraints. The scheduling constraints were listed on a
sheet of paper which was read to the subject and then
posted on the board for reference. In some of the
experimental conditions, the computer continuously
checked the subject's schedule and displayed text and
graphical messages if it violated any of the constraints.
Figure 6. The four experimental conditions: Paper and Reduced-
Senseboard (top row, left to right), and Pen-GUI and Senseboard
(bottom).
Experimental Design
Subjects performed our task under four different
conditions, which we designed to be as similar as possible
in overall size, orientation, and general physical setup.
Figure 6 shows the four conditions. We used a withinsubjects
design, with each subject performing the tasks
under all four conditions and analyzed the results with a
two-factor (condition x subject) ANOVA. We
systematically varied the orders in which subjects
performed the four conditions as well as the assignment of
the four schedule variations to the four conditions. We
created four schedule variations of our task, with different
names and randomly-generated differences in their skill
sets, to reduce the learning effect for each subject, and
assigned the variations differently to the four different
conditions for each subject.
Paper Condition: One current practice for scheduling tasks
is to use pieces of paper or sticky notes, as was done at
CHI. To compare tangible interaction with real physical
interaction, we created a version of our experimental task
that used conventional paper sticky notes placed on the
same vertical board. We had designed the experimental
task to require only moving pucks on the board, without
pressing or stamping, so that we could have a paper version
of the interface for comparison. We pre-printed sticky
notes with the same information that was projected onto the
pucks (name and skills), in the same size as the pucks, and
placed them on the Senseboard itself, which is inactive in
this condition, but provides a vertical surface, physical
arrangement, and grid identical to the Senseboard
condition.
Reduced-Senseboard Condition: An issue with most
tangible user interfaces is that their simulation of the real
world is imperfect, and we expect this to have some
performance cost. In our case, compared to paper sticky
notes, there is latency between the user's action and the
visible result, possible misregistration of the data projected
onto the physical object, lower resolution of the projected
display compared to real paper, and loss of the displayed
data when the pucks are off the board. Each of these can be
addressed with technological improvements (including
ways for the computer to write more permanently on the
pucks), but, for now, we see our TUI as having many—but
not all—of the advantages of paper. We expect some
performance decrement due to this imperfect simulation.
To measure it, we introduce a condition in which the
Senseboard merely emulates the paper sticky notes with the
computational enhancements removed. That is, the user
moves the same pucks, with their projected labels, but the
computer does not display messages if the schedule
violates the constraints. We expect this condition to
perform worse than paper and worse than regular
Senseboard, but to provide a way to tease apart these
components of performance.
Pen-GUI Condition: We also compared our system to a
more conventional GUI-like computer interface. We
wanted a condition closely matched to the physical
arrangement of the Senseboard, so we used a digital
whiteboard, rather than a regular mouse and keyboard. We
used a Microfield Graphics Inc. Softboard 201™, a
whiteboard that can track the location of a (non-marking)
pen across its surface. We placed the projector at the same
height as the Senseboard projector, and tilted the board
slightly upward just like the Senseboard. We developed a
GUI, drag-and-drop style interface for our task, using the
same Java program, modified to allow interaction by
dragging the pen on the board instead of the Senseboard
pucks, but with the same projected data display, and same
size and physical arrangement of the board.
We tried to give this system nearly all the properties of the
Senseboard except for tangible pucks. The result was an
interface that was not quite a conventional mouse-andkeyboard
GUI, but was closer to the Senseboard in ways
that probably improve its performance on this task over a
conventional GUI. Like a touchscreen, it provides moredirect interaction than a mouse, and it had somewhat better
latency and registration than the Senseboard. For these
reasons, we expected it to be an attractive choice. What it
lacks is the physical manipulation of the pucks or sticky
notes, representing data items that can be held in the user's
hand, which can help him or her think about the problem.
Senseboard Condition: Finally, we used our Senseboard as
described in the previous section. The projector was placed
2 m. above the floor, and the board tilted slightly upward.
We used the original Bannou Pro pucks, since this task did
not use the pressing and stamping functionality of our new
pucks.
Subjects
We used 13 subjects, 6 male and 7 female, recruited with
posters on bulletin boards around the MIT campus.
Sessions lasted 30-45 minutes; we paid each subject $10.
Procedure
The task was presented to subjects as a set of sticky notes,
pucks, or projected GUI data, arranged in the same initial
configuration. For each condition, we explained the task to
the subject, demonstrated the operation of the equipment,
and then asked the subject to perform the task. Our system
measured elapsed time to perform the task and recorded the
final schedule they created (and also recorded every move
of a puck or data item, for further investigation). For the
paper condition, the computer simply recorded elapsed
time for uniformity, and we transcribed the arrangement of
the sticky notes manually. We then asked the subject to fill
out a questionnaire about the task and the four conditions
and interviewed them to elicit any further comments or
thoughts.
250
200
150
100
50
0
114.9
Paper
164.3
Reduced-
Senseboard
101.7
Senseboard
139.8
Pen-GUI
Figure 7. Experimental results: time (in seconds) to complete the
task for the four experimental conditions, mean and 95%
confidence interval. Note that the first three conditions use
tangible interaction to varying degrees; the last two use computer
augmentation; only the Senseboard condition uses both.
Results
We collected subjects' performance on each of the four
tasks, questionnaire answers, and interview comments. We
found that subjects completed nearly all tasks correctly (8
errors out of 780 assignments) so we used the time to
complete the task as an overall performance measure.
Figure 7 shows the mean time for each condition. The
trends we expected are suggested by these data, though the
results are only weakly statistically significant (ANOVA
for condition effect gives F(3,36) = 2.147, p = 0.11). We
also analyzed the data for the effect of order of presentation
or learning, and found a weak effect (p = 0.08), but as
noted we had counterbalanced the orders across subjects
and conditions for this. Finally we checked to make sure
there was no effect of the four schedule variations and
found none (p = 0.32).
Our questionnaire asked subjects to rate how well they
liked each of the four conditions on a 7-point Likert scale.
We found a significant overall effect of condition (F(3,36)
= 3.368, p = 0.03), a weak preference for Senseboard over
each of the other three conditions, and a substantial dislike
of the Paper condition. The questionnaire also asked
subjects about the strong and weak points of each of the
conditions. Many commented on the value of manipulating
the physical tags to aid in thinking; a typical response was:
“I like the idea of manipulating something, makes it easier
to tell who you're scheduling where.” Some subjects were
also attracted to the Pen-GUI because they had never seen
such a device. Finally, some subjects complained that their
head and hands sometimes blocked the projected data; this
applied uniformly to all our conditions except Paper. Our
plans to mount the projector on the ceiling, aimed
downward at 30-45 degrees, and tilt the board upward to
match will greatly reduce this problem.
Discussion
The two current user interfaces most likely to be used today
for a task like ours are paper notes or computer GUI. Each
has strengths and weaknesses, but it is difficult to blend
their strengths—to combine fluid, physical manipulation or
“tangible thinking” with the features of computer
automation to yield better performance than either
alternative alone. Our results suggest, though at weak
statistical significance levels, that the new tangible
interface (Senseboard condition) may indeed yield better
performance than either pure physical (Paper) or GUI
(Pen).
In comparing TUI to GUI, we opted for a GUI condition
that closely matched the physical setup of the Senseboard,
to eliminate extraneous factors from the comparison. It was
thus not a pure GUI, but a hybrid that included some
properties of the TUI. It allowed more direct interaction
(touching the screen) than a mouse (at a cost of likely arm
fatigue for a longer task than ours). Our particular
implementation also had somewhat less latency than the
Senseboard and less critical registration requirements for
the projected image. We believe that these factors
enhanced its performance relative to the TUI and that a
conventional GUI with a mouse and desktop display would
have given slightly worse performance, but it might be a
valuable condition for future study because it is so widely
used. It would also be interesting to compare a “Reduced-
GUI” condition (graphical manipulation but no computer
augmentation), analogous to our Reduced-Senseboardcondition, though we would expect still worse
performance.
In comparing TUI to paper, we believe the tangible pucks
preserve some of the fluidity and “tangible thinking”
qualities of paper, but do so incompletely. We see a small
improvement for TUI over paper and then use the
Reduced-Senseboard condition to attempt to decompose
that improvement into two larger component parts. We
decompose this difference into (1) the price paid for the
imperfect way TUI simulates paper (difference between
Paper and Reduced-Senseboard) and (2) the benefit gained
from automation (difference between Reduced-Senseboard
and complete Senseboard). We also view this
decomposition as measuring the value of “natural”
interaction (Paper), minus the cost of simulating it
(Reduced-Senseboard), plus the benefit of “artificial”
additions. As the price paid for the imperfect simulation is
reduced by future technological developments, such as
lower latency, better display technology, and new bistable
display materials that would let the pucks retain their
displayed information, the advantage for TUI can become
stronger, while the benefit of the automation will be
retained.
CONCLUSIONS
Senseboard shows how features of physical manipulation
(collaboration and rapid, fluid, two-handed free-form
manipulation) and digital interaction (display statistics and
implications, check constraints, view details, group and
ungroup, import, export, save, restore, sort) can be blended
into a single platform that suggests better performance than
either alternative alone. We presented our system design,
interaction techniques, design rationale, example
application, and an experiment to study it. Our conference
paper task is representative of a larger class of generic
arranging, organizing, or grouping tasks for which this type
of interface should be useful, such as papers for a literature
search, pages for a web site design, files on a computer
disk, slides for a presentation, employees to be reorganized,
or ideas in a brainstorming session. We demonstrate how a
tangible user interface can be provided for this kind of
abstract or non-geometric problem domain to yield better
performance than existing alternatives.
ACKNOWLEDGMENTS
We thank Kenji Alt, Katherine Butler, Angela Chang, Flora
Chiu, Zahra Kanji, and Gustavo Santos for their work on
hardware and software for the Senseboard; and our
colleagues in the Tangible Media Group at MIT, including
Brygg Ullmer, Mike Ananny, and Dan Maynes-Aminzade;
and in the Supply Chain Visualization project at Intel and
at MIT Sloan School for discussions, suggestions, and
help. This work was supported by the Things That Think
and Digital Life Consortia of the MIT Media Lab and the
Intel Corporation.
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