ISSN 1744-1986
T e c h n i c a l R e p o r t N o 2 0 0 8 / 1 9
Augmenting Collaborative Tasks with Shareable Interactive Surfaces
Richard Morris
19th December 2008
Student Research Proposal
Department of Computing Faculty of Mathematics and Computing The Open University Walton Hall, Milton Keynes MK7 6AA United Kingdom http://computing.open.ac.uk
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"Figure 1: Extended McGrath Circumplex (Ward, Marshall and Novick, 1995)
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"Figure 2: Description of Task Classification
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RTI:8RBF>FB;"=:KKED:9E8>7S"S9:<I"GBGDB9F"C:<K;"EKK"REAB"E"F>G>KE9"<7;B9F8E7;>7S":X"8RB";E8E"E7;">8F"9BI9BFB78E8>:7F4"E7;"8R>F"C:<K;"EKK:C"EKK"
GBGDB9F":X"8RB"S9:<I"8:">78B9E=8"F>G<K8E7B:<FKT4":9"8EJ>7S"8<97F"EF"8RBT"BYIKE>7":9"BYIK:9B"E7">;BE?"
!Q"
"Figure 3: ‘Mindmap’ Illustrating Possible Task Types and Issues in Addressing Research Question
To the right are the numbers of the extended McGrath definitions outlined in 2.2.1 illustrating an example
of how these tasks cover a wide range. Contests/Battles (number 7) has not been included as this was not
viewed to be a collaborative task.
!"
]"
Q"
["
!W"
M4"N"
6"
@"
!]"
"
Table 1: The Spectrum of Tasks
+DF89E=8" (>7E7=>EK"+7EKTF>F"
w" LBF>S7"d"Z9E>7F8:9G>7S"
x"#9>G>7EK"-7ABF8>SE8>:7""
`.>YB;"<FB":X":DhB=8Fa"
v &:<9>F8")XX>=B"
#:7=9B8B" 2>;B:"'BXKB=8>:7"`*I:98F"#:E=R>7Sa"
"
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!?"-7":9;B9"8:"FBB"8RB"S9BE8BF8"BXXB=8"F>VB":X"8EFJ"8TIB"EK:7S"8RB"EDF89E=8O=:7=9B8B"
FIB=89<G"-"FREKK"X:=<F"GT"9BFBE9=R":7"8RB"BY89BGB"B7;F?"
A3030 :9+1&%#1(5.&+2+(M*-#&.1.(
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RB9B">F"8:"9BXKB=8"E7;">GI9:AB"<7;B9F8E7;>7S"`FBB"P>F8:7"E7;"nB>=R7B94"!]][a?"
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X>7E7=>EK">7X:9GE8>:7":9":8RB9"KE9SB";E8EFB8F"CRB9B4"=<99B78KT4">7;>A>;<EK"8::KF"
E9B"<FB;"DT"S9:<IF4"F<=R"EF"E"FI9BE;FRBB8":7"E"/#?"&R>F"EIIBE9F"8:"KB7;">8FBKX"8:"
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EIIK>=E8>:7?"+F"GB78>:7B;"ED:AB4"8RBFB"8C:"8EFJF"E9B"=R:FB7"8:"I9:A>;B"
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6W"
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`!]][a"E7;",E88:7"E7;"*G>8R"`!]]@a"REAB"F<SSBF8B;"8RE8"9BXKB=8>:7"=E7":==<9">7"
;>XXB9B78"8>GBX9EGBF4"X9:G">7F8E78"9BE=8>:7"8:"K:7SO8B9G"=:7F>;B9E8>:7"E7;"
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I9:A>;>7S"E"DEF>F"X:9"9BX9EG>7S"<7;B9F8E7;>7S":AB9"8>GB?"&RB">FF<B":X"R:C"
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(9:G"8RB">78B9A>BCF"E7;":DFB9AE8>:7F4"-"IKE7"8:"SB7B9E8B"RTI:8RBFBF"ED:<8"=:KK:=E8B;"=:KKED:9E8>:7"<F>7S"*-L$*"X:9"=:7=9B8B"E7;"EDF89E=8"8EFJF"8RE8"8EJB"
>78:"E==:<78"8RB"X:KK:C>7SU"
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• &RB"BXXB=8"C>KK";>XXB9"X:9"EDF89E=8"E7;"=:7=9B8B"8EFJF4"C>8R"8RB"X:9GB9"
FR:C>7S"E"S9BE8B9"DB7BX>8">7"h<;SBGB78"E7;"8RB"KE88B9">7"9BXKB=8>:7?"&R>F">F"
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A37 !.+.%&#$(421#*;.+(
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:XU"o ,:C";>XXB9B78"8EFJ"8TIBF"EXXB=8"=:KK:=E8B;"=:KKED:9E8>AB"C:9Jg"
o ,:C"8RB"EXX:9;E7=B":X"*-L$*"EXXB=8"=:KK:=E8B;"=:KKED:9E8>AB"C:9Jg"
"
• -"EKF:"IKE7"8:";BABK:I"RB<9>F8>=F"X:9"8RB";BF>S7"E7;"BAEK<E8>:7":X"*-L$*"
EIIK>=E8>:7F"X:9"=:KK:=E8B;"=:KKED:9E8>AB"C:9J?"
6!"
B !.+.%&#$().1$*'*?*GH(
&R>F"FB=8>:7":<8K>7BF"8RB"GB8R:;F"E7;"8B=R7>^<BF"I9:I:FB;"8:"E=R>BAB"8RB"
:DhB=8>ABF"E7;":<8=:GBF";BF=9>DB;">7"*B=8>:7"M?"
&R9BB"IREFBF":X"C:9J"E9B"IKE77B;U"
/REFB"!U" -7ABF8>SE8>7S"&EFJF"
/REFB"6U" #:7;<=8>7S"$YIB9>GB78F"
/REFB"MU" $AEK<E8>:7"E7;"(9EGBC:9J"#:7F:K>;E8>:7"
(:9"8RB"X>9F8"IREFB4"-"IKE7"8:"=:7;<=8"FBAB9EK">78B9A>BCF"E7;":DFB9AE8>:7EK"
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KE9SB"EDF89E=8";E8E"FB8F?"&RB"FB=:7;"IREFB"C>KK">7A:KAB"=E99T>7S":<8"=:789:KKB;"
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BYIKE7E8:9T"X9EGBC:9J?"
-"REAB"EK9BE;T"=:7;<=8B;"FBAB9EK">78B9A>BCF"`FBB"DBK:Ca">7":9;B9"8:"KBE97"G:9B"ED:<8"8C:"BY>F8>7S"8EFJ"8TIBFU"FI:98F"=:E=R>7S"E7;"X>7E7=>EK"X:9B=EF8>7S?"&R>F"
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F:K<8>:7?"
B30 8$%+.(0J(,-5.+1"G%1"-G(F%+>+(
B3030 8&.?";"-%&H(,-1.&5".6+(M*;<?.1.'(
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&RB"SB7B9EK"X>7;>7S":X"8RB"8C:"FI:98F"=:E=R>7S">78B9A>BCF"=:GIKB8B;">F"8RE8":7B"
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:X"=:KKED:9E8>:7"E9:<7;">78B9E=8>AB"8EDKB8:IF"C:<K;"DB"DB7BX>=>EK?"
-7"8B9GF":X"EDF89E=8"8EFJ"8TIBF4"+7;9BC"/>IB9">F"9BFI:7F>DKB"X:9"=:GG<7>=E8>7S"
D<F>7BFF"E7EKT8>=F"8:">7ABF8:9F"E7;")0"RBE;F?"-7"8RB">78B9A>BC"RB";BF=9>DB;"
=:GG:7"F=B7E9>:F"CRB9B"RB"G<F8";>F=<FF"8RB"9BF<K8F":X"R>F"E7EKTFBF":X"D<F>7BFF";E8E"E7;"B7F<9B"8RE8"EKK"GBGDB9F":X"E"S9:<I"REAB"E7"<7;B9F8E7;>7S":X"E"=:GIKBY"
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=:KKED:9E8>:74"fCRE8O>Xc"E7EKTFBF"E7;";>9B=8"GE7>I<KE8>:7"CR>=R"EKK:C"E";BBIB9"
<7;B9F8E7;>7S":X"8RB";E8E"E7;"8RB"9BKE8>:7FR>IF"8RB9B>7?"&R>F"=:<K;">7A:KAB"R>F"<F>7S"8RB"8EDKB8:I"8:"I9BFB78";E8E"E7;";>FIKET";>XXB9B78"A>F<EK>FE8>:7F"E7;"
89E7FX:9GE8>:7F4":9"8:"EKK:C"8RB"E<;>B7=B"8:"BYIK:9B"8RB";E8E"DT"8RBGFBKABF?"
66"
B3037 I212&.(,-1.&5".6+(
.:9B">7O;BI8R">78B9A>BCF"E9B"IKE77B;"X:9";>XXB9B78"8EFJF"`FBB"+IIB7;>Y"+Ha?"
*:GB":X"8RBFB"IKE77B;">78B9A>BCF"REAB"=:GB"ED:<8"X9:G">7X:9GE8>:7"SE8RB9B;">7"
8RB"I9BK>G>7E9T">78B9A>BCF?"&RB"I:FF>D>K>8T":X"=RE7SBF">7"8RB";>9B=8>:7":X"8RB"
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<7;B9F8E7;>7S":X"I9BA>:<F"C:9J">7"8RBFB"E9BEF4"CR>=R"EAB7<BF":X"BYIK:9E8>:7"
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BYIKE7E8:9T"X9EGBC:9J"X:9"8RB"DBF8"<FB":X"*-L$*?"
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8RB"8EFJ"8TIBF"-"C:<K;"K>JB"8:"F8<;T4"E7;"8R>F"GET"I9BAB78"E7">78B9BF8>7S"EAB7<B"
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C>KK"89T"E7;"BYIK:9B"EF"GE7T";>XXB9B78"EAB7<BF"EF"I:FF>DKB"E7;"FBKB=8"8RB"G:F8"
I9:G>F>7S"E7;"FB=<9B":7BF"EF"8RB"C:9J"I9:S9BFFBF?"+"I9>GE9T"S:EK":X"8RB"
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8EDKB8:I"EIIK>=E8>:7F?"
-7">78B9A>BC>7S"8RBFB"IB:IKB"-"C>KK"89T"E7;"EF=B98E>7"CRE8">78B9E=8>:7F"E9B"G:F8"
=:GG:7"E7;"CR>=R"=:<K;"DB"G:F8"BXXB=8>ABKT"8<97B;">78:"8EDKB8:I"EIIK>=E8>:7F?"
$YEGIKBF":X"8RB"J>7;":X"^<BF8>:7F"-"IKE7"8:"EFJ"8:"BK>=>8"8R>F">7X:9GE8>:7"E9BU"
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E7;"R:C";:BF"8B=R7:K:ST"F<II:98"8RBFB">78B9E=8>:7Fi"
• +9B"8RB9B"E7T">FF<BF"T:<"=E7">;B78>XT"C>8R"=<99B78"8B=R7:K:ST"F:K<8>:7Fi"
• _RE8"X:9G";:BF"8RB"=:KKED:9E8>:7"8EJBi"L:BF">8">7A:KAB"B^<>8EDKB"<FB":X"
8RB"8B=R7:K:STi"+9B"EKK"GBGDB9F":X"8RB"=:KKED:9E8>AB"S9:<I"FJ>KKB;"E8"
<F>7S"8RB"8B=R7:K:STi""
&RB"7EGBF"E7;"=:GIE7>BF":X"IKE77B;">78B9A>BCF"X:9"FBAB9EK"8EFJ"8TIBF"E9B"
:<8K>7BF">7"+IIB7;>Y"+[?"
B37 8$%+.(7J(E/<.&";.-1%?(=12'".+(
_RB7"F<>8EDKB"<FB9"S9:<IF"E7;"8EFJ"8TIBF"REAB"DBB7">;B78>X>B;"E7;"
:DFB9AE8>:7EK"F8<;>BF":X"8RBFB"REAB"8EJB7"IKE=B"-"C>KK"DBS>7"IKE77>7S"=:789:KKB;"
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$%&''#()%*'#
10 11 12 13 14 2009 16 17 18 19 20 21 22 23 24 25 2010 27 28 29 30 31 32
Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul
Phase 0: Literature Review
Task Taxonomy
Framework
Development
Phase 1:
Interviews & Observation
Concrete Tasks
Abstract Tasks
Phase 2: Experimentation
Requirements Analysis
Coding / Prototyping
Framework / Testing
Phase 3: Analysis and
Framework Development
Coding and stats
Framework
Consolidation
Writing Up
Internship
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Sidanius, J. and Pratto, F. (1999) Social Dominance: An Intergroup Theory of Social
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Framing Fluidity: The Flow of Ideas across Interactions ???
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ABSTRACT
The concept of fluidity is increasingly being used in the HCI
literature to describe a desired state of user experience,
especially for new interfaces such as multi-touch surfaces and
pervasive technologies. Design principles and guidelines have
been suggested to achieve optimally fluid interactions.
However, the term is in a state of flux and has proven difficult
to operationalise. Means of evaluation and the true value of
fluid interaction are still in debate. In this paper, we provide a
critique of the various uses of the term fluidity, focussing on the
flow of ideas across interactions. To analyse fluidity we
propose a framework, comprising three heuristics: ready-
presence ratio, cognitive focus and interaction matrices. We
show how this can be used to examine the both the user’s
cognitive state when carrying out tasks and the switching of
attention between interface and conversation in collaborative
settings and learning environments.
Categories and Subject Descriptors
H.1.2 [User/Machine Systems]: human factors, human
information processing, software psychology.
H.5.2 [Information Interfaces and Presentation (e.g., HCI)]:
user interfaces.
General Terms
Measurement, Design, Human Factors, Theory.
Keywords
Fluidity, User Experience, Interaction, Interface Design
1. INTRODUCTION “The hope is that in not too many years, human brains and
computing machines will be coupled together very tightly,
and that the resulting partnership will think as no human
brain has ever thought.” (J.C.R. Licklider, 1960)
The way we interact with computers and what we do with them
is changing at a rapid rate. Only 20 years ago, most of us used a
PC primarily to launch a word processing application in order
to create and edit text. Much of HCI research at the time
followed suit, focusing on how to design usable interfaces that
could make text editing interfaces easier for users [e.g., 8].
Efficiency, effectiveness, learnability, utility and safety emerged
as the core usability principles guiding interface design [41].
Design principles such as visibility, affordance, feedback and
consistency also came to the fore [41].
Since the ‘80s, our everyday and work lives have become
suffused with technologies of every shape and form, from
mobile phones to large multi-touch displays, performing all
manner of functions. In an attempt to keep up with the
technological advances, a central concern in current day HCI is
to consider what other kinds of principles are now needed to
guide the design of the diversity of new human-computer
interactions [10].
A development has been the emergence of a plethora of terms
that describe more subjective aspects of the user experience
when interacting with a technology, such as aesthetically
pleasing, rewarding, motivating and emotionally fulfilling [41].
Higher-level concepts have also entered the HCI vocabulary
that aim to define desirable properties of interfaces, such as
findability [34], ambiguity [15] and shareability [41]. One that
is increasingly being used in the literature to describe the
fundamental aspects of interaction is ‘fluidity,’ for example;
“dialog boxes and prompts...hinder fluid interaction” [19, pg.
3], “occlusion can be an obstacle to fluid interaction with a
shared object.” [40, pg. 9] and “a flexible set of tools allowing
fluid transitions between views is required to fully
support...collaboration” [43, pg. 10].
Fluidity in general parlance means the physical property of a
substance that enables it to flow. In HCI, this is the property of
an interface which allows ideas and information to flow.
Similar to how affordance became widely used in the HCI
community [36], the term is being increasingly used by
researchers, designers and practitioners. One of the reasons is
its accessibility; it encapsulates tangible properties that are easy
to imagine and which are suggestive of a variety of phenomena,
such as the flow of data, interaction and thoughts, in a
continuous way and across a period of time. The term can be
used to express things that are otherwise difficult to articulate.
However, the problem of using fluidity in such a loose way is it
becomes difficult to operationalise in any meaningful sense.
Within HCI, it has been used to describe a range of different
phenomena, including: the smooth transfer of data between
devices [7]; the avoidance of interruptions to the user’s
workflow [19, 6]; the naturalness of an interface in relation to
its functions [12]; supporting the transitions between activities
[40] through boundaries [28] and with animation [6, 17, 23].
For it to have utility as a guiding principle it is necessary to be
more precise in defining its qualities, i.e., how it is possible to
allow an idea or expression to flow from the user through a
machine. The goal of this paper is to explicate the concept of
fluidity in the context of HCI and suggest a framework for how
it can be operationalised to inform both the design and
evaluation of interactions for the diversity of emerging
ubiquitous technologies.
Throughout this paper we examine the effects of the users’ level
of expertise, their interactions with the task, the interfaces of the
various technologies at play, the physical environment and their
interactions with other people. A focus is on the level of task
complexity the technology or interface is designed for, and how
© The Authors 2007.
many assumptions the designer or software makes about the
users and how the tools will be used. Following our critique, we
present three complimentary heuristics that are intended to be
used as analytic measures of fluidity and illustrate how they can
be used through worked examples.
2. FROM SEAMLESSNESS TO FLOW Mark Weiser’s vision of ubiquitous computing centred around
the idea of seamless interactions that are rendered invisible to
the user:
“A good tool is an invisible tool. By invisible, I mean that
the tool does not intrude on your consciousness; you focus
on the task, not the tool...” [44, pg. 1]
He went on to say how the disappearance should not be a result
of the technology per se, but of cognition [45]. Information
would appear in the centre of our attention when needed and
disappear into the periphery of our attention when not.
Moreover, when things disappear into the background people
can use them without thinking, which allows them to focus on
higher-level goals.
Since Weiser’s seminal article, the notion of seamlessness has
frequently been used to refer to what technology should be like
in the future, namely, without distinct boundaries between
devices and functions. However, as noted by [32], Weiser was
not necessarily endorsing a concept of seamlessness in the
sense of a physical property of the technology. The point is that
computing systems need not hide their visible boundaries to
conceal their shortcomings and, equally, it may be desirable to
have seamfulness where the context is appropriate. Indeed, it is
possible to have ‘beautiful seams’ [9], such as being able to
choose which cell mast your mobile phone will connect to
instead of the phone software, being led by the assumptions of
its designer, choosing for you.
It has been argued that the ideal situation would be when
technologies are “literally visible, effectively invisible” [9]. The
idea of seamlessness has also been recast in terms of
ordinariness [13], in the sense that ordinary things do not
intrude into our consciousness whereas extraordinary things do.
Related to ordinariness is the property of expertise or
automaticity – things which are familiar to us or which we are
expert with are psychologically different compared with novel
things. And when couched in terms of expertise, the notion of
flow becomes relevant.
Czikszentmihalyi [11] first described how individuals can
achieve a state of flow by engaging in a task where their skills
are matched to the task(s) – somewhere between the states of
anxiety and boredom. These ‘flow experiences’ are subjectively
described as enjoyable and creative by the individuals
experiencing them, which suggests that this would be a good
state for a computer user to be in. A computer interface which
allows users to enter a ‘flow state’ is one which is performing
well and not frustrating them. Reviewing the literature, fluidity
has sometimes been used to represent this quality of ‘flow’ in
using an interface, when the user is not frustrated, bored or
restricted by the limitations of the interface [3, 6, 17, 37].
Reliably designing for these states has been difficult. This is
where the notion of fluidity has been brought to bear.
3. FROM TRANQUIL INTERACTION TO
EVER-READY One of the first researchers in HCI to use the phrase fluidity
was Guimbretière [19] who drew on an analogy to how we
interact with physical tools:
“Based on our observations of fluid expert interactions from
everyday life, such as driving a car or playing a violin, we
have designed and built a fluid interaction framework which
encourages gesture memory, reduces the need for dialog
with the user, and provides a scoping mechanism for modes.
Together, these features progressively make the cognitive
load of using the interface disappear. The user becomes free
to focus on other tasks, the same way one can drive a car
while conversing with a passenger.” [18, pg. iv]
Applying this idea to the design of computer interfaces
Guimbretière developed a framework that comprised of five
principles thought to lead to more fluidity. These are reducing
cognitive load, avoiding temporal modes, avoiding dialogs,
developing graceful latency management and avoiding visual
artefacts.
Reducing Cognitive Load
By simplifying commands and helping the user become familiar
with the interface more quickly, cognitive load can be reduced,
freeing up the user to focus on higher-order goals.
Avoiding Temporal Modes
If an interface has different modes which persist over time, such
as selecting a paint or text tool in a graphics package, or using
the Caps Lock key, a user might become confused momentarily
if their work is interrupted or if they forget which temporal
mode they are working in. Spatial modes or quasi-modes (using
the Shift key is an example of a quasi-mode) are considered to
be preferable for fluid interfaces.
Avoiding Dialogs
Dialogs appear when the application requires some clarification
from the user or to notify the user of a state change. These can
distract the user and require direct manipulation to deal with
(i.e. clicking the OK button) and therefore interrupt the flow of
work. Additionally, on large screens a dialog box may go
unnoticed and cause confusion when it interferes with the
users’ area of focus [4].
Developing Graceful Latency Management
If a user has to wait more than a few tenths of a second after
issuing a command for it to be completed by the computer they
change their interaction style, slowing down their interactions
and checking to see if their commands were issued correctly.
Guimbretière suggests that, since users are good at switching
between tasks, providing low-fidelity feedback on the state of
their running commands and providing the ability to switch
between tasks is a way of overcoming these problems [25].
This is not just an issue of speed. An interaction can be slow
but also fluid. For example, interfaces that are deliberately
designed to forcefully slow down the interaction, such as the
Drift Table [16] and pHotOluck [32] have the effect of shifting
the users’ attention to other aspects of the interaction which are
related to higher order functions of a task, like reflecting on
their experience or socialising more.
Avoiding Visual Artefacts
Visual clutter from toolbars, scrollbars, icons and palettes can
distract the user and obstruct their interaction by taking up a lot
of otherwise usable screen real estate. By focusing on the
immediacy of performing simple tasks like writing, and
providing gesture-based menus like Guimbretière’s FlowMenu
[20], the visual attention of the user can be focused better.
These five principles instruct mostly about things to avoid –
things which might interrupt the flow of activity, which might
be an ongoing discussion, for example. A group of people
interacting with a wall display, for example, are also engaged in
a conversation with each other about what they are designing.
Their focus on the computer interface is likely to be sporadic
rather than continuous. Moreover, they don’t want to be
distracted from the conversation by having to pay too much
attention to what has to be done at the interface. Activities like
brainstorming depend on unhampered dynamic social
interaction and the ability of participants to readily switch from
one medium to another. If an interface is designed to avoid
interruptions and reduce cognitive demands on users it is more
likely they will be able to focus on their primary task – be it
planning, designing, socialising or other. The result of
achieving this is a more ‘tranquil’ interaction.
When an interface is more tranquil, e.g., it does not throw up
dialog boxes as frequently, the interaction has a higher
probability of being fluid, thus allowing for the continued
thought and expression of the user to be held in a high
cognitive state. In contrast, a ‘turbulent’ interface requires users
to constantly change their focus of attention and deal with low-
level objects of the interface. A tranquil interface enables
greater attention to be placed on higher-order goals and social
interaction. This is seen in emerging interfaces, such as tabletop
displays, which are aimed at collaboration and feature less
visual ‘furniture’ and dialog boxes.
4. REALITY-BASED INTERFACES The emerging class of new technologies such as interactive
tabletops, wall displays and augmented reality offer a multitude
of interfaces to be designed and integrated. The new interaction
styles that are becoming available, such as gesture, multi-touch,
two-handed input, etc. are capable of exploiting people’s pre-
existing knowledge of the everyday, non-digital world far more
than desktop computers did.
"Just think of the brain cells you don't have to devote to
remembering the syntax of the user interface. You can
devote those brain cells to the job you are trying to do.”
(Jacob, in [3], pg .1)
Designers must decide which models, metaphors and
interaction methods to use with these new input and output
techniques in order to enable such fluid interactions to
materialise, from the many available. A recent approach that
has been advocated is reality-based interaction [27], which
models real-world themes and seeks to reduce the gulf of
execution [36, 22], namely, the gap between a user’s goals for
action and the means to execute those goals. The real-world
themes are naïve physics, body awareness, environmental
awareness and social awareness.
Naïve Physics
Experience from the real world teaches people about the
relations between certain physical properties such as mass,
gravity, inertia and elasticity. This innate knowledge can be
used to operate an interface which shows the same qualities as a
real-world object, e.g., the scrolling feature in the Apple iPhone
which continues scrolling as if it had mass. An example of a
new interface that has used physical modelling at the desktop is
the BumpTop platform [1]. This has been designed to allow
users to pick up, stack, throw, drop and collide objects such as
files on a 3D modelled virtual desktop.
Body Awareness and Skills
People have the ability to co-ordinate their limbs in an
unconscious manner and also have a proprioceptive sense of
where their limbs are in relation to their body without having to
look. Input techniques can be designed to exploit these skills,
including two-handed and whole body interaction.
Environmental Awareness and Skills
When people perform tasks, whether computationally-aided or
not, they have a sense of the context they and the task hold in
the surrounding environment. Mobile or collaborative
interfaces could take into account the environment in which
they are being used and adjust to variables such as the number
of people in the room, or the surrounding noise level.
Social Awareness and Skills
New shareable technologies are being developed to support
collaborative tasks such as tabletop displays (e.g. Mitsubishi
DiamondTouch and Microsoft Surface) that allow users to
interact with the interface with increased eye-contact and
awareness of other collaborators.
By designing more intuitive interfaces, based on the rules of
real-world dynamics, the need for low-level operational
expertise can be reduced providing the user with more
opportunities to focus on higher-order goals and more focused
creativity. It should also help users, if interrupted, to get back to
where they were before the interruption, as the cognitive effort
of getting back into the framework of the interaction is reduced.
There is also a benefit to stepping back and viewing the bigger
picture [33] to facilitate learning or to take a fresh perspective.
Jacob notes, however, that certain trade-offs have to be made
when designing reality-based interfaces in order to provide the
complex functionality of advanced software packages. An
interface should not be unnecessarily complicated, and should
employ reality-based interaction as much as possible, except
where certain explicit trade-offs are made to add further
functionality. Jacob uses the analogy of the character
Superman. When he is performing simple tasks he walks and
talks like a regular human. But when the situation calls for more
power he can use X-ray vision or fly to increase his efficiency
in completing his task.
5. INCREASING FUNCTIONALITY AND
EXPERTISE Microsoft’s Surface technology has been demonstrated using an
example of placing a photo camera on a table whereby the
photos from that camera are displayed in an arc around it. Then,
by placing a phone on the table and dragging and flicking a
picture towards it one can transfer the photo from camera to
phone without having to deal with the machines at any point1.
This is an example of a fluid interaction which uses physical
modelling and gestures to create an intuitive way of completing
a task which, as most are familiar with, can involve a lot of
dialog boxes, selecting devices and menu selections in a more
awkward desktop interface. However, this task is quite simple.
One cannot, for example, imagine an architect using AutoCAD
to design a house in this way.
However, watching an expert AutoCAD user might give the
impression of the same ease of interaction between the user and
the interface. Here, the expert user knows which dialog boxes
will come up when they perform certain actions such as
selecting a material or choosing a thickness of a metal girder.
The user is also engaged with the machine in a one-on-one
manner giving it his full attention and using a mouse and
keyboard to make fine-grained inputs. The flexibility of the
AutoCAD tool lets the architect create any structure they wish,
as the assumptions made on the part of the software developer
do not extend to simplifying the design process by limiting
choice of materials or dimensions. As Guimbretière states,
1 see http://youtube.com/watch?v=rP5y7yp06n0
dialog boxes, tool selections, object handles etc. are “inevitable
to provide complex functionality” as “the traditional
workstation GUI is oriented toward facilitating the speed of
highly differentiated actions done by experienced users whose
attention focus is entirely on the current computer activity.”
[19, pg. 3]
Consider all the properties a ‘perfect’ machine would have:
efficiency, flexibility, fluidity, good ergonomics, easy
upgradeability, low cost, etc. However, there is a trade-off
which touches on Jacob’s ‘Superman’ analogy. The more
complex a task is the more input it requires from the user and
the less the designer of the application can make assumptions
about how their tool is going to be used. Hence, an interface
should be designed to be as fluid as possible up to the point that
it is over-simplified and compromises flexibility and features
that an expert user would expect.
There is also a trade-off to be made in terms of the immediacy
of the interface to a novice user and providing means for the
interface to assist learning so that novice users can become
more expert. Bederson [6] describes interfaces which support
the ‘flow’ experience as defined by Csikszentmihalyi. Anderson
[2] describes one hypothesised process by which a novice
becomes an expert. The first stage is called the ‘cognitive stage’
where a learner is working using explicit conscious directions
and thoughts. This could be made intuitive by using reality-
based interaction. The next stage is called the ‘associative
stage’ where the learner can apply a domain-specific skill based
on previous experience without having to think through how
they will complete the task. The final stage is the ‘autonomous
stage’ where the skill becomes automated and rapid, and the
conscious cognitive effort and control is minimised. This is
where a computer user can operate without feedback, for
example, when using keyboard shortcuts. As an example of the
whole process, one can become skilled at squash by practicing
different swings, moves and shots until they become
‘automatic’ and can be executed swiftly and in full without
conscious load on the player, thus freeing the player’s thoughts
to consider higher order goals like positioning and defence.
The flip-side to simplifying an interface is that it can negatively
affect the process whereby a novice user can become expert.
Removing menu bars in an effort to reduce visual clutter can
hamper learning in the associative stage; options in menus
which are ‘greyed out’ according to which tool is selected are
still visible and can act as reminders and a means for learners to
associate actions and properties. This type of learning facilitates
exploration of features which the learner is not yet familiar with
and ‘scaffolds’ their learning as they explore functions [24].
Guimbretière’s FlowMenu [20] neatly gives visual feedback
without permanent menu bars or palettes by using a pen-based
radial layout menu system which encircles the pointer whenever
the menu is summoned but also allows experts to use gestural
memory without feedback.
This approach to interacting with pen- and touch-enabled
surfaces suggests that it is possible for these new post-WIMP
technologies to afford even more fluidity than traditional
desktops: these pen-gestures provide access to the elevated
‘superman’ functionality for expert users. However, these
gestures are more expressive than keyboard shortcuts. Imagine
a designer being able to draw a line in one material and with the
other hand change material while still drawing and without
even lifting their pen. This possibility brings to mind Chalmers
and MacColl: “the ultimate design goal...a good tool lets users
focus on their task – even when that task involves changing the
tool itself.” [9, pg.7]
However, the fluidity of the interaction is not governed just by
the quality of the interaction which the user has with the
interface but also the interactions between the user and others,
and other users and the interface. A successful collaborative
task may depend on the ability of individuals to work singly in
personal spaces while carefully choosing their interactions with
the other users at various stages. How is it possible to
conceptualise fluidity in terms of the integration of
technologies, people and tasks? Group settings, where there are
two or more people interacting with an interface and with each
other, are especially complex: the users’ attention is split
between social and computer interaction and interruptions from
outside sources, as well as changes in tracks of thought, can be
disruptive if the interface is too demanding.
6. GROUP INTERACTIONS
In group settings, interruptions in the conversation can happen
quite often [39]. Nowadays, people often check their mobile
phones for messages during a conversation, while others may
enter the room to pass on messages. This requires the group of
users to get back into the particular zone or train of thought
they were on just before they were interrupted. Interruptions
can come from other sources, such as having to deal with
technical problems, and misunderstandings in social
interactions. A highly fluid interface will be able to facilitate
getting back up to speed quickly and accurately. Being able to
provide buffers or cues for users to transition easily between
fluid and non-fluid states, or to take breaks and resume when
refreshed is important.
Focusing the users’ attention and providing sensory cues is one
way to assist users in these transitions. However, if, for
example, the primary user is interrupted because the technology
breaks while they are giving a presentation, or working in a
collaborative group, the stress / nervous arousal from the
frustration and the effect of being observed while struggling
with the technology may hamper the effectiveness of these
sensory cues and make it harder to get back into the ‘zone’.
It is often assumed that tasks which might benefit from a highly
fluid interface are ones which involve a creative process or an
aspect of learning [4, 16, 19, 33, 43]. In these cases it can be
useful for individuals to purposely interrupt themselves and
take a step back to see the bigger picture. Then it becomes more
important to support the user in getting back into their previous
state of mind and/or resuming the group activity after they have
stopped and gained a wider perspective [c.f. 29]. This transition
from working through the interface on a design, to stepping
back and looking at the machine and the ideas represented
therein with a broader scope is an important aspect of how
fluidity, and assisting the movement of the user in and out of
the interaction, can be beneficial to learning and overall goal
evaluation.
Given how complex group interactions can be, it is a very
challenging to design a computer interface which can both
support group interaction at the same time as being simple
enough to use so that all group members can contribute without
being overly frustrated. Giving a collaborative interface the
affordance of paper in these scenarios is difficult enough.
However, with careful thought about the ways in which ideas
will flow into and out of the interface, and the different levels
of expertise the users will have and the flexibility they will
require in using an interface, technology can surpass paper, as
shown in Guimbretière’s studies. Good human-computer
interaction is necessary for good social interaction. Making an
interface as intuitive as picking up a pen and writing allows
equitable participation and better group dynamics [e.g., 38].
7. ANALYSING FLUIDITY So far we have examined how to design reality-based and
intuitive interfaces that can support fluid interactions, for high
level tasks, from single user to group settings for both simple to
complex applications. How do we begin to analyse and test
interfaces to determine if they support fluid interactions? Is it
possible to assess whether one interface affords a higher level
of fluidity than another – in ways that we are able to determine
if one kind of word processor or mobile phone is more usable
than another? In this section we propose three heuristics that
can be used to analyse fluidity.
7.1 Ready-presence Ratio The first heuristic is based on the idea of measuring interactions
when moving between subjective states of involvement; our
starting point is Heidegger’s well known concepts of readiness-
to-hand and presence-at-hand. The canonical example of using
a concrete tool such as a hammer exemplifies what it means to
switch between ‘present-at-hand’ and ‘ready-to-hand’
depending on the user’s awareness of the hammer [see 14, 33].
When hammering away at a nail one is often not aware of the
hammer as being distinct from one’s own arm and hand or part
of our ‘totality of involvements’. The tool becomes an
extension of ourselves in the expression of our task, in this
case, driving a nail into something. In this state the hammer is
ready-to-hand. However, should the hammer break or hit our
thumb we would become aware of the interruption to our task
and the hammer would become present-at-hand.
In terms of user interactions, we can use this idea to
conceptualise when a user is interrupted in the flow of
completing their task, e.g., finding they are using the type tool
when they wanted the draw tool because they were unaware of
the temporal mode of the system; the tool moves from ready-to-
hand to present-at-hand.
So how can we analyse these switches between states? We
propose that user actions which are directly related to dealing
creatively with a task can be considered as higher order and
those which are directed at dealing with the state of the
computer as lower order. Crudely speaking, an interaction
where the ratio of higher order to lower order actions is greater
than another is said to be more fluid, or have greater fluidity. In
this sense, fluidity is essentially the property of being in a
higher cognitive state, focused on the task over the period of the
task as opposed to dealing with the lower order states of the
machine:
The key feature of fluidity is that it is a measure of the
unbroken chain of task-specific actions and cognitions. The
formula shows the ratio of the frequency of operations which
are in the purpose of high-level goals of the task to the low-
level object-based operations. For example, if a user is to draw
a circle and label it with text, they might perform 15 operations
dealing with low level aspects of the machine such as opening
the program, selecting the appropriate view and palette,
selecting the right tool, and changing to the text tool, and the
operations which are related to the higher-order goal such as
drawing the circle or typing the text would amount to two. This
would give a fluidity score of F=-0.77 ((2-15)/17).
Compare this to performing a similar task on a drawing surface
such as Guimbretière’s PostBrainstom interface. The lower-
order task would be picking up the pen, but drawing the circle
and writing the text would be done directly as two higher order
goal-centred operations, giving a fluidity score of F=0.67. The
difference in the fluidity score F is large, with the latter
interface in a more positive direction, indicating that it leads to
a more fluid interaction.
This example illustrates how a user of a certain experience level
would interact with the two interfaces. However, if the user
were an expert in the first interface but novice in the second the
result would appear somewhat at odds to how you would see
them carry out the tasks. That is to say that there is an
interaction between user experience and interface design.
This heuristic can provide a way of assessing the fluidity of new
reality-based interfaces in terms of whether they support high or
low levels. Imagine a new style tabletop post-WIMP interface.
Assuming that it is highly reality-based, both novice and expert
users should achieve a similar F score, no matter what their
difference in experience level. Any difference in F score would
indicate that higher functionality is being employed and that
access to this functionality and how it can be learned should be
addressed.
When defining and analysing fluid human-computer
interactions, therefore, it is important to take into account the
users’ level of expertise with the task and the technology. It
may be possible to design interfaces that are fluid to use by
experts for a task (e.g., a games console) but not for novices.
There is a distinction also between expertise at lower and
higher levels. For example, being an expert typist may not
automatically confer an advantage to a player in a strategy game
if they are not expert at the higher-level goals and conventions
of the game. Conversely, an expert tennis player might be at a
disadvantage in a game of Wii Tennis against someone who has
more expertise in using the WiiMote controller.
There are some odd effects to consider when applying this
analysis of fluidity to certain applications. For example,
researchers using large displays (
[19, 4] found that users were confused or made mistakes when
dialog boxes or notifications were displayed outside of their
visual field. This is an oversight of the interface design which
leads to lower overall fluidity. However, a user who knew that
their action was likely to result in an error box or dialog would
know to look for it. Being an expert can also lead to a greater
resilience to being disrupted by interruptions through
familiarity with interface quirks.
7.2 Cognitive Focus Our second heuristic is to graphically project cognitive focus
over time in an interaction. Figure 1, below, shows an example
of how an experienced user might interact with a complicated
application like AutoCAD. After launching the application the
user can begin drawing and setting up a basic outline whilst in a
high-order cognitive stage and thinking about the goals of their
design. However, a time must come where the user has to
specify a certain variable and a specific dialogue must be
sought where the user can input a variable such as wall
thickness, or choose a material. Because the user is experienced
and knows what to expect they can interact smoothly and
without feedback or cogitation. Like Jacob’s Superman the
architect must make a small but useful interruption to their flow
and focus to make an explicit input which is not expressible in a
reality-based interaction manner.
fluidity = higher-order – lower-order
total operations
Figure 2 shows an individual sharing photos with someone else
using a tabletop display such as a Microsoft Surface with an
inturruption in the middle of the task. The graph is intended to
highlight the difference between the users’ experience of
interacting with the table at times when low-level objects must
be dealt with, such as waiting for data transfer or changing the
power state of the machine, and being able to operate on the
higher-order goals of the task, the actual photo sharing and
discussion.
Following the interruption and then resuming the machine from
its standby state, a short period of time is spent by both users
looking back over the photos in the stack. This is an example of
how the user experience can be ‘buffered’ when moving back
into an interaction, whereby remembering the state of the
interface before the interruption is stored and the position of
photos relative to each other can jog the users’ memories and
help in resuming the conversational thread. This could be
enhanced further by, for example, replaying recorded audio
from before the interruption to further assist recollection.
Figure 2. Cognitive focus over time. In this example one
person is sharing photos using Microsoft Surface with
another person. At point A the camera is placed on the table
and the photos are fanned out. At point B the user is sifting
through the photos to find the ones they want to share. At C
the user is showing and discussing the photos with the other
person. At point D an interruption occurs and the task is
halted. At E the user has to touch the screen because it has
gone into standby state and at F they sift through the last
few photos to retrieve their conversational thread. At G the
sharing and discussion continues. At H the users are waiting
while the photos are transferred to a mobile phone.
7.3 Interaction Matrices Our third heuristic, interaction matrices, analyzes the
interactions between groups of users when using various
interfaces. As already noted, supporting a collaborative design
task requires the ability to move from working one-on-one with
the computer, to social interaction, and three-way or higher-
order interaction with the interface. In this context, fluidity
impacts on the quality of an interaction that extends beyond the
user-interface, as the properties of this interaction can have an
effect on social interactions, collaboration and the flow of ideas.
A user who is experiencing a fluid interaction with an interface
will find it easier to take part in the social level of interaction,
facilitating good all-round interaction and more comprehensive
collaboration.
Figure 3 depicts several modes of interaction using a short-hand
notation. Situation ‘A’ is the simplest, where one user and one
interface are having one interaction {1:1}. In ‘B’ there are three
users all interacting with both the interface and each other. The
dotted lines on the interface are meant to denote that there are
different possible ways to divide the work area. All three users
could be sharing the one interface together {(3*3):1}, or they
could be working in separate spaces and sharing between each
others’ spaces {(3*3):(3*3)}, or simply working on their private
Figure 1. Cognitive focus over time in an interaction. The
line represents the user’s level of cognitive involvement in
the interaction. At point A the user is opening the
application, dealing with low-level objects of the system. At
point B the user has begun thinking on the abstract levels of
the goals they wish to achieve in this task. At point C the
user has had to deal with a task-specific dialog such as
entering a value into a dialog box.
A
B
C
Cognitive focus
Higher order
Specific
variable
selection
Low level
Time
Interruption D
E
G
H
F
A
B
C
Cognitive focus
Higher order
Specific
variable
selection
Low level
Time
Figure 1. Different interaction modes. In A the user interacts with the computer. In B, two or more users interact with each
other and with the computer in either a shared or a private mode. In C a single user interacts with the computer and with a
separate group who can dip in and out of interacting with the computer. In D a single user interacts with the computer to
create a visualisation for the separate group to use in their interaction.
A. 1:1 B. (3*3):1 or (3*3):(3*3) or (3*3):(1*3)
C. (3*3):1 D. (3*3):1:1
spaces alone {(3*3):(1*3)}. In ‘C’ the users are interacting with
each other but one user is mainly interacting with the interface.
Situation ‘D’ is a special situation where an expert ‘superman’
user is interacting with the interface in a way the other group
cannot and the output of this interaction is used by the group
{(3*3):1:1}. This describes the situation in which a note-taker
or facilitator can create a hypertext representation of the
arguments and facts being used in the group discussion. This is
the same situation as used by Shum [42] with the application
‘Compendium’ which creates visualisations of a discussion and
the arguments used therein.
All these situations have different modes of interaction, but a
fluid interaction between the user and the interface always
benefits the entire goal, whether the user is in a group, alone,
novice or expert.
In ‘D’ the user is required to be highly expert as creating real-
time visualisations of discussions is a complicated task. The
user is one of Jacob’s ‘Supermen’. However, in ‘B’ simpler
actions must be used to ensure that everyone is at a similar level
of control of the interface. The interface should be more
tranquil as if a dialog box pops up, it is unclear which user it
corresponds to. In ‘A’ the user can be novice or expert,
depending on their level of experience and the necessity for
complex ‘superpower’ operations. ‘C’ is in-between as the main
user can fall on a range of expertise but other users may wish to
input directly.
The interaction matrices can be used to describe how different
user / interface combinations can lead to different design goals
and expectations about fluidity. By separating the interaction
matrices inside and outside the interface a clearer understanding
can be reached of the true nature of interaction occurring. When
a single user is using a single interface, there is scope to make
the interaction highly fluid but also to extend functionality
using ‘Superman’ elevated commands. With a larger group
there is less scope for complex functionality as the whole user
group must be catered for at the same level.
7.4 Using the Heuristics The three heuristics above are intended to assist both in the
design and evaluation of interfaces and the various types of
interactions, and group modes, by expressing different aspects
of the fluidity of these interactions. The ready-presence ratio
focuses the thought of the designer on the way a user
experiences readiness-to-hand, when focused on the higher-
order goals of the task, and presence-at-hand, when the user
experience changes to see themselves and the tool (interface)
separately. This can then be used in tandem with the guidelines
produced by other authors, and enhance them by increasing
understanding of the shifts in conscious awareness of the
user(s). It also assists in evaluation of the overall interaction, as
shown, and provides a means to analyse the equivalence of
reality-based interfaces for novice and expert users.
The cognitive focus graph can help in highlighting the
transitions between users’ states of awareness and presence in
the interaction, to help identify key areas where the design of
the interface could help these transitions. The area under the
graph also gives an evaluative indication of the overall fluidity
of the interface, where a larger area indicates greater time spent
in goal-focused states of mind. By adjusting for the total length
of time of the interaction, it could be possible to analyse
interactions in a way which is less skewed by experience level,
in terms of dealing with dialog boxes etc., than the ready-
presence ratio.
The interaction matrices heuristic can be useful in designing an
interface by considering the multiplicity of ways that groups
and single users can interact with it and with each other. By
separating the interactions ‘inside’ and ‘outside’ of the interface
it can be seen where design goals such as removing visual
clutter will be effective. It also provides a shorthand way of
expressing specific interaction modes which can help in
discussion and evaluation.
8. CONCLUSION Our critique of fluidity has shown how the term has come to be
used to express various aspects of human-computer interaction,
but often without being precisely operationalised. Sometimes
the term has been used to mean ‘flow’ in the sense that the user
can extend their thought to the higher-order goals of the task,
and a subjective experience of ease and pleasure when using an
interface. The design principles suggested by Guimbretière are
valuable constraints for improving interfaces, especially with
post-WIMP technologies, but do not provide specific or
particularly helpful means of analysing the resulting interaction.
Fluidity was re-expressed in terms of the users’ state of
cognition and how it can be conceptualised as allowing better
collaboration by helping design interfaces which allow
intermittent attention between interface and conversation to
happen whilst keeping the creative thoughts and expressions
‘flowing’.
Design guidelines are also emerging to help develop more fluid
interfaces. For example, Scott, et al. have produced a series of
guidelines for designing tabletop interfaces [40], where the
concept of fluidity is expressed as a list of principles with a
focus on lowering the cognitive overhead of making transitions
from one representation to another, from group work to shared
work or moving between activities. Isenberg et al. [26] have
noted that these guidelines can be expressed in the positive
sense of supporting high-level cognitive aspects of a task
without forcing the user to deal with low-level objects.
Complementing this work, in our paper we have proposed three
new heuristics intended to guide analysis of interfaces to
determine their level of fluidity for different settings, namely
ready-presence ratio, cognitive focus and interaction matrices.
Each is intended to ‘raise the consciousness’ of the designer
and theorist alike in thinking about the flow of ideas across
interactions and the ultimate expressive goal of the new
interfaces we are designing. In addition to using heuristic
analysis, we also need new ways of empirically measuring
fluidity.
Computers can be used as creative instruments, and like the
design of musical instruments which have evolved over many
years, or how car design allows us to hold a conversation whilst
driving, it is likely that more fluid interactions with machines
will enhance and express this creativity more clearly. In
particular, with tabletop and wall displays becoming more
‘ordinary’ technology, and with the affordances they provide in
terms of shareability, fluidity will become an increasingly
important concept.
As a final thought; perhaps it is a limit of the brain that it wants
to function in a continuous manner [see 5 and 31], but this can
be accommodated by a fluid interface, by supporting transitions
between tasks [40] and avoiding interruptions [18]. However, it
is important to keep in mind that there is a value in being able
to make guided pauses in interactions to adjust perspectives on
the work, enhance learning, or to refresh the eyes and mind, as
long as these transitions can be supported or ‘buffered’ by the
interface. It is certainly necessary for interface designers to
ensure users are helped when moving in and out of interaction,
as interruptions are certain to happen.
9. ACKNOWLEDGMENTS
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END OF PAPER
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Analysing Fluid Interaction across Multiple Displays Richard Morris, Paul Marshall and Yvonne Rogers
Open University Milton Keynes, UK
ABSTRACT
Interaction with groups carrying out tasks across multiple
displays and devices can be complex. Users have to switch their
attention from controlling one device to another while
continuing with their ongoing activity and conversations. This
raises questions about how to support and evaluate interface
design which facilitate fluid interaction. This paper provides a
nascent framework of fluidity as a way of analysing interactions
across multiple displays and tasks. Three fluidity heuristics are
outlined illustrating how they can be used to aid the design and
evaluation of interactions with multi-display systems.
1. INTRODUCTION
Shareable and personal devices are providing designers with
new opportunities for creating a wide range of rich technology-
augmented spaces that can support collaborative working,
learning or playing. However, there are significant challenges in
doing so: infrastructure and interfaces must be developed to
share information, representations and interactions across an
increasingly diverse ecology of devices. Furthermore, this
diversification leads to a combinatorial explosion of factors that
the designer must take into account when developing such a
system for a user group, task or context. Such factors include
the number of devices available to the users; what kinds of
information should be shared and what should be private; what
mechanism or metaphor should be used to move information
between devices; and in what orientation should shared displays
be placed. As pointed out by Tan et al. [6] there is a dearth of
evaluation methods, tasks and metrics that could be used in
evaluating multi-device collaborative environments.
A key problem is managing the flow of work between displays,
be they personal/small or shared/large displays, specifically
how one addresses the other displays, and transfers work, from
the one currently in use. Will they be controlled through
gestures (if touch-enabled) or menus? Will animation help in
reducing the cognitive overhead of switching between screens?
How will the users be given feedback or retrieve their work if
something goes wrong? Our research seeks to help designers
address these questions by providing conceptual tools of
analysis.
2. BACKGROUND
Fluidity is a concept that is increasingly being used to describe
a desired state for new forms of interaction. This would be
manifest in ways such as users being able to move smoothly
between displays, devices and tasks without having to exert too
much cognitive effort. In particular, users should not have to
constantly switch their attention between control operations and
the goals of the task. The aim is to enable a group’s actions and
interactions with a system to be invisible (cognitively), ordinary
and to flow smoothly. While this is an important goal, the
concept has yet to be operationalized so that it is possible to
assess the fluidity of the diversity of interactions when using
multiple displays.
Fluidity has been used to describe the various transitions that
are needed to enable collaboration [7] and the obstacles that can
hinder interactions, such as dialog boxes popping up [1] and as
Isenberg et al. [3] have noted that these guidelines can be
expressed in the positive sense of supporting high-level
cognitive aspects of a task without forcing the user to deal with
low-level objects. The benefit of such fluidity of interaction is
that users can bring more of their attention and creativity to
bear on their ultimate goals, or other demands such as
collaboration, leading to more productivity and higher quality
work.
One approach to fluid interface design is in terms of reality-
based interaction [4]. This seeks to model real-world themes
and to reduce the gap between a user’s goals and the means of
execution. The real-world themes are naïve physics, body
awareness, environmental awareness and social awareness. By
designing interfaces, based on the rules of these dynamics, the
need for low-level operational expertise is reduced, affording
the user the opportunity to focus on higher-order goals and
more focused creativity. Also, it should be easier for users to
return to where they were previously when interrupted, as the
cognitive effort of getting back into the framework of the
interaction is reduced. This also affords the benefit of
encouraging reflection and viewing the bigger picture for a
fresh perspective or learning. As these interfaces provide more
natural interaction it is also hypothesised that they will lead to
better social interaction when working in groups.
It follows that multiple display and device systems should not
be unnecessarily complicated, and should employ reality-based
interaction where possible, except where certain explicit trade-
offs are made to add further functionality. Jacob uses the
analogy of the character Superman: when he is performing
simple tasks he walks and talks like a regular human, but when
the situation requires it he uses his powers to increase his
efficiency in completing his task.
The concept of fluidity is appropriate for analysing the complex
development of multi-user, multi-device interactions. One
challenge is to provide a way for users to get the most out of the
technology at novice and expert levels. Too little help or
signposting and the novice cannot engage with the system: too
much and the expert user becomes frustrated. Guimbretière
argues that dialog boxes, tool selections, object handles etc. are
“inevitable to provide complex functionality” [1, pg. 3]. His
FlowMenu [2] gives visual feedback without permanent menu
bars or palettes by using a pen-addressed radial layout menu,
which encircles the pointer whenever the menu is summoned
but also allows experts to use gestural memory without
feedback.
However, collaboration is not governed solely by the quality of
the interaction that the user has with the interface but also the
interactions between the user and others, and other users and
the interface. A successful collaborative task may depend on the
ability of individuals to work singly in personal spaces while
carefully choosing their interactions with the other users at
various stages. Given the intricacy of group interactions,
another challenge is to design computer interfaces which can
support them while being simple enough to use that all group
members can contribute effectively.
3. FLUIDITY HEURISTICS Below we propose three heuristics that can be used to analyse
how systems of multiple displays and devices are able to
support users in achieving their task goals. These are ready-
presence ratio, cognitive focus maps and interaction matrices.
3.1 Ready-presence Ratio The first heuristic, ready-presence ratio, is based on the idea of
measuring interactions when moving between subjective states
of involvement: our starting point is Heidegger’s well known
concepts of readiness-to-hand and presence-at-hand (see also
[8]). The canonical example of using a concrete tool such as a
hammer exemplifies what it means to switch between ‘present-
at-hand’ and ‘ready-to-hand’ depending on the user’s
awareness of the hammer. When hammering away at a nail one
is often not aware of the hammer as being distinct from one’s
own arm and hand or part of our ‘totality of involvements’. The
tool becomes an extension of ourselves in the expression of our
task. In this state the hammer is ready-to-hand. However,
should the hammer break or hit our thumb we would become
aware of the interruption to our task and the hammer would
become present-at-hand.
In terms of user interactions, we employ this idea to
conceptualise when a user is interrupted in the flow of
completing their task. Higher-order user actions are those
directly related to dealing creatively with a task; those which
are directed at dealing with the state of the computer are lower-
order. Expressed as a ratio of higher- to lower-order action,
fluidity is essentially the property of being in a higher cognitive
state and focused on the task, not the tool. Thus:
The key feature of fluidity is that it is a measure of the
proportion of task-specific actions and cognition. For example,
if a user is to draw a circle and label it with text, they might
perform 15 operations dealing with low level aspects of the
machine such as opening the program, selecting the appropriate
view and palette, selecting the right tool, and changing to the
text tool, and the operations which are related to the higher-
order goal such as drawing the circle or typing the text would
amount to two. This would give a fluidity score of F=-0.77
(2-15)/17).
Compare this to performing a similar task on a drawing surface
such as Guimbretière’s PostBrainstom interface [1]. The lower-
order task would be picking up the pen, but drawing the circle
and writing the text would be done directly as two higher-order
goal-centred operations, giving a fluidity score of F=0.33.
Compared to the previous example the fluidity score F is large,
and in a more positive direction, indicating that it leads to a
more fluid interaction.
As well as comparing across interfaces, this heuristic is also
intended to be applied across experience levels. Supposing that
a new interface is highly reality-based then experience level
should have less of an effect on the F score. Any difference in F
could indicate that experienced users are employing shortcuts,
which could indicate an area for further study.
When defining and analysing fluid human-computer
interactions, therefore, it is important to take into account the
users’ level of expertise with the task and the technology. It
may be possible to design interfaces that are fluid to use by
experts for a task but not for novices (e.g., a games console).
There is a distinction also between expertise at lower and
higher levels of action. For example, being an expert typist may
not automatically confer an advantage to a player in a strategy
game if they are not also expert at the higher-level goals and
conventions of the game. Conversely, an expert tennis player
might be at a disadvantage in a game of Wii Tennis against
someone who has more expertise in using the WiiMote
controller.
3.2 Cognitive Focus Maps The second heuristic, cognitive focus maps, graphically project
cognitive focus over time in an interaction. Figure 1 (top) shows
an example of how an experienced user might interact with a
complicated application like AutoCAD. After launching the
application the user can begin outlining whilst in a high-order
cognitive state and considering their design goals. Next the user
has to specify a certain variable and a specific dialogue must be
sought where the user can input a variable e.g. wall thickness,
or material type. Because the user is experienced and knows
what to expect they can interact smoothly and without feedback
or cogitation. Like Jacob’s Superman the architect must make a
small but useful interruption to their flow to make an explicit
input.
Figure 1. Cognitive focus over time in an interaction for
(top) an experienced user and (bottom) during an
interruption.
Figure 1 (bottom) describes a difference scenario where an
individual is sharing photos with someone else using a tabletop
display such as a Microsoft Surface with an inturruption in the
middle of the task. The figure is intended to highlight the
difference between the users’ experience of interacting with the
table at times when low-level objects must be dealt with, such
as waiting for data transfer or resuming the machine after it
goes into standby during the interruption, and being able to
operate on the higher-order goals of the task such as the actual
photo sharing and discussion.
Following the interruption and resuming the machine from its
standby state, a short period of time is spent by both users
looking back over the photos in the stack. This is an example of
fluidity = higher-order – lower-order
total operations
how the user experience can be ‘buffered’ when moving back
into an interaction, whereby remembering the state of the
interface before the interruption and the position of photos
relative to each other can aid the users’ memories and help in
resuming the conversational thread. This could be enhanced
further by, for example, replaying recorded audio from before
the interruption to assist recollection.
3.3 Interaction Matrices Our third heuristic, interaction matrices, describes the
interactions between groups of users with various interfaces.
Supporting a collaborative design task requires the ability to
move from working one-on-one with the computer, to social
interaction, and multi-user interaction with the interface. In this
context, fluidity impacts on the quality of an interaction that
extends beyond the user-interface, as the properties of
interaction ‘inside the interface’ can have an effect on social
interactions ‘outside’, collaboration and the flow of ideas. Thus
a user who is experiencing a fluid interaction with an interface
will find it easier to take part in the social level of interaction,
theoretically leading to better collaboration.
Figure 2 depicts several modes of interaction using a short-hand
notation, or interaction matrix, taking the form {(‘outside’
interactions):(interface interactions)}. Situation ‘A’ is the
simplest: one user and one interface are having one interaction
{1:1}. In ‘B’ there are three users all interacting with both the
interface and each other. The dotted lines on the interface are
meant to denote that there are different ways to divide the work
area. All three users could be sharing the one interface together
{(3*3):1} or they could be working in separate spaces and
sharing between each others’ spaces {(3*3):(3*3)}, or simply
working on their private spaces alone {(3*3):(1*3)}. In ‘C’ the
users are interacting with each other but one user is mainly
interacting with the interface.
Situation ‘D’ is a special situation where an expert user is
interacting with the interface in a way the other group cannot
and the output of this interaction is used by the group
{(3*3):1:1}, such as when using a facilitator.
The interaction matrices can be used to describe how different
user / interface combinations can lead to different design goals
and expectations about fluidity. By separating the interaction
matrices inside and outside the interface a clearer understanding
can be reached of the true nature of interaction occurring. All
these situations have different modes of interaction, but a fluid
interaction between the user and the interface always benefits
the entire goal, whether the user is in a group, alone, novice or
expert. In ‘D’ the user is required to be highly expert as creating
real-time visualisations of discussions is a complicated task.
However, in ‘B’ simpler interface actions should be used to
ensure all users have a similar level of control. Also, the
interface should avoid dialog boxes, as it may be unclear which
user it corresponds to. In ‘A’ the user can be novice or expert,
depending on their level of experience and the necessity for
complex ‘superpower’ operations. ‘C’ is in-between as the main
user can fall on a range of expertise but other users may wish to
input directly.
4. USING THE HEURISTICS Our fluidity heuristics are intended to assist both in the design
and evaluation of interfaces and the various types of
interactions, and group modes, by expressing different aspects
of the fluidity of these interactions. The ready-presence ratio is
intended to focus the designer on the way a user experiences
readiness-to-hand, when focused on the higher-order goals of
the task, and presence-at-hand – seeing the user and the tool
(interface) separately. This heuristic can be used in tandem with
the guidelines produced by other authors (e.g. [1],[5]) to assist
understanding of users’ shifts in conscious awareness at key
points. It assists in evaluation of the overall interaction quality
and in comparing across interfaces or user experience levels.
The cognitive focus map can help in highlighting the transitions
between users’ states of awareness and ‘presence’ in the
interaction, to help identify key areas in the design of the
interface to enhance the user experience. The area under the
graph also gives an evaluative indication of the overall fluidity
of the interface, where a larger area indicates greater time spent
in goal-focused states of mind. By adjusting for the total length
of time of the interaction, it could be possible to analyse
interactions in a way which is less skewed by experience level,
in terms of dealing with dialog boxes etc., than the ready-
presence ratio.
The interaction matrices heuristic can be useful in designing an
interface by highlighting the ways that groups and single users
can interact with it and with each other. By separating the
interactions inside and outside of the interface it can be seen
where design goals, such as removing visual clutter, will be
most effective. It also provides a shorthand way of expressing
specific interaction modes to help facilitate discussion and
evaluation.
To illustrate how these heuristics can be used together to
analyse how fluid the interactions are for users moving between
displays consider the scenario of how scheduling work
meetings could be enhanced through having a system of shared
and personal displays. People in organisations use shared
software calendars to arrange projects, meetings and schedules
of work. However, it can be very time consuming to arrange a
meeting, especially when it depends on email response. If a
shared calendar application was made available whereby a large
touchscreen could display an overall work schedule (i.e. a Gantt
chart), representatives from each team could work either on the
overview schedule or on small tablet or handheld devices to
make fine-scale adjustments or to rearrange outside
commitments around the emerging work schedule. The
application could be analysed by using the three heuristics
above. The interaction matrices would help in describing the
different permutations of interaction possible in this
Figure 1. Different interaction modes and associated interaction matrices.
A. 1:1 B. (3*3):(3*3) or (3*3):(1*3) C. (3*3):1 D. (3*3):1:1
arrangement, i.e. whether the users are all interacting with the
large screen, their small screens or any combination between.
This could assist a designer focus their methods for moving
data between screens at the most appropriate times.
The fluidity of the interaction could be assessed for each
individual user using the ready-presence ratio. This would give
an impression of how different styles of interface would support
or hinder fluid interaction for any given situation. For example,
when working on a small personal screen the user may have to
make more low-level actions due to the size constraint of the
interface, but this may lead to more rapid progression of the
overall goal of organisation on the main chart.
The cognitive focus maps can be used to analyse the interaction
over time and to bring attention to key moments, such as when
a user switches between working at the big screen to their
individual screen, or to help design ways for users to
collaborate or resume work after an interruption. Explicitly
considering where the user is focusing their attention at certain
points can help the interface designer support key actions.
One problem which may arise when collaboratively creating
schedules is that a clash may arise. Being able to work on their
own sub-schedules individually, the team members involved
can work in parallel to make fine adjustments and compromise
to make the overall schedule work, and this could be expressed
in an interaction matrix. Key points in this interaction would be
the identifying of the clash on the main screen. Then the users
would have to use the interface to edit their schedules
individually and then return their change to the main schedule.
How this is accomplished through interface design choices can
be readily assessed using the ready-presence ratio and cognitive
focus maps. Experimental studies could then be performed on
different interface prototypes to evaluate their fluidity.
5. SUMMARY
We propose that in order for groups to effectively utilise
multiple displays by switching work between screens, interfaces
and interaction styles and be able to do so without interrupting
the flow of their ongoing tasks, the interactions have to be fluid.
However, fluidity can be a nebulous term that is difficult to
define. In this paper we propose three heuristics intended to aid
in the analysis of interface and task interactions, which can
provide an indication of fluidity and clarify the processes
involved. In so doing, they can highlight how to design for
users so they can easily transition between multiple interfaces,
tasks and conversation whilst keeping their creative thoughts
and expressions ‘flowing’.
6. ACKNOWLEDGMENTS
This work is supported by the EPSRC who are funding the
ShareIT project (shareitproject.org).
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