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Heuristic evaluation of virtual reality applications


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This paper presents a heuristic method for evaluating virtual environment (VE) user interfaces. The method is based on Nielsen's [Usability Inspection Methods, 1994] usability heuristics, extended by VE-specific principles proposed by Sutcliffe and Kaur [Behaviour and Information Technology 19 (2000) 415–426]. Twelve heuristics are presented which address usability and presence issues. An inspection-based evaluation method is described and illustrated with three usability case study assessments, the last of which rates the applicability and validity of the heuristics by several evaluators. Use of the method uncovered several usability problems and trapped the most serious errors. Finally, VE applications integrating measures of usability and presence are discussed.
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Heuristic evaluation of virtual reality applications
Alistair Sutcliffe*, Brian Gault
Centre for HCI Design, School of Informatics, University of Manchester,
P.O. Box 88, Manchester M60 1QD, UK
Received 25 July 2003; revised 30 April 2004; accepted 1 May 2004
Available online 20 June 2004
This paper presents a heuristic method for evaluating virtual environment (VE) user interfaces.
The method is based on Nielsen’s [Usability Inspection Methods, 1994] usability heuristics,
extended by VE-specific principles proposed by Sutcliffe and Kaur [Behaviour and Information
Technology 19 (2000) 415426]. Twelve heuristics are presented which address usability and
presence issues. An inspection-based evaluation method is described and illustrated with three
usability case study assessments, the last of which rates the applicability and validity of the heuristics
by several evaluators. Use of the method uncovered several usability problems and trapped the most
serious errors. Finally, VE applications integrating measures of usability and presence are discussed.
q2004 Elsevier B.V. All rights reserved.
Keywords: CAVE; Virtual environment; Heuristic evaluation; Usability
1. Introduction
Several studies have highlighted usability problems associated with the use of virtual
environments (VEs) (Gabbard and Hix, 1997), while field studies of VR designers have
demonstrated the need for HCI knowledge and methods (Kaur et al., 1996). Others have
shown that the designers of VE systems cannot rely solely on the methods developed for
standard graphical user interfaces (GUIs) since their interaction styles are radically
different from standard user interfaces (Bowman and Hodges, 1997; Poupyrev and
Ichikawa, 1999). Most studies have followed observation and expert interpretation of
users’ errors (Hix et al., 1999) or experimental studies reporting performance data and
problems in a range of VE technology (Bowman et al., 1999). Design principles have been
0953-5438/$ - see front matter q2004 Elsevier B.V. All rights reserved.
Interacting with Computers 16 (2004) 831–849
*Corresponding author. Tel.: þ44-0-161-200-3315; fax: þ44-0þ161-200-3324.
E-mail address: (A. Sutcliffe).
applied to evaluate desktop VR applications (Johnson, 1998); and checklist evaluation
methods, based on Nielsen’s heuristics (1994), have been adapted for VR (Kalawsky,
1999); however, no evaluation heuristics have been proposed specialised for VEs.
Quality assessment of VEs has also focused on assessment of presence, i.e. evaluating
how real or natural the user’s experience was when immersed in the environment.
Presence has been evaluated by questionnaires which ask users to rate various qualities
of the VE ranging from perceptions of ‘being there’ (Slater et al., 1996) to more detailed
inventories ranking controls, feedback, perception of realism and user engagement
(Witmer and Singer, 1999). While presence measures can benchmark VE designs in
terms of their realism and overall user experience, they do not help to diagnose design
flaws for formative evaluation. In this paper, we propose a set of heuristics and an expert
evaluation method that follows the current widely accepted approach for user interface
evaluation (Nielsen, 2000), but extend it specifically for VEs. We also attempt to unify
the formative evaluation with presence assessment in VR. The paper describes the
heuristic evaluation method for VR and two case studies in which it has been applied.
The paper is structured in three sections. First, we describe the heuristics and the
evaluation method, and then two case studies illustrating its application. This is followed
by an assessment of the method by multiple experts to assess the reliability of the
heuristics and the utility of the method itself. The paper concludes with a discussion of
future research on evaluating VEs.
2. Heuristics for VE evaluation
Usability inspection is defined as “the generic name for a set of methods based on having
evaluators inspect or examine usability-related aspects of a user interface” (Nielsen and
Molich, 1990). Inspection-based methods may use guidelines or checklists as criteria to
discover usability problems (e.g. ISO, 1997, part 16; Ravden and Johnson, 1989), although
deciding which guidelines are applicable to particular problems becomes more difficult as
the number of guidelines increase. In contrast, heuristic evaluation methods are quicker to
use since they employ a limited set of design principles or heuristics. Since heuristic
evaluation is quick, it is a cost-effective method and traps a high proportion of usability
problems with 4 5 trained evaluators (Nielsen, 1993), although this approach is not as
effective as usability testing with real users (Wharton et al., 1994). Heuristic evaluation can
be performed by one usability expert although studies have shown that the effectiveness of
the method is significantly improved by involving multiple evaluators (Nielsen, 1993).
The heuristics used in these studies are derived from Nielsen (1994) and our
previous work on VR design principles (Sutcliffe and Kaur, 2000). Nielsen’s (1993)
heuristics formed the basis on which we developed the VR customised heuristics, some
of which have a direct mapping, e.g. match between system and real world and our
heuristics 1 and 2; others have an indirect influence, e.g. consistency and standards,
user control and freedom, and visibility of system status. However, our heuristics were
motivated by the different nature of VEs, in particular, the need for intuitive interaction
and the sense of immersion, which is important for many VR applications that aim to
simulate reality as faithfully as possible (Stone, 2002). The heuristics, with a brief
A. Sutcliffe, B. Gault / Interacting with Computers 16 (2004) 831–849832
explanation, are:
1. Natural engagement. Interaction should approach the user’s expectation of
interaction in the real world as far as possible. Ideally, the user should be
unaware that the reality is virtual. Interpreting this heuristic will depend on the
naturalness requirement and the user’s sense of presence and engagement.
2. Compatibility with the user’s task and domain. The VE and behaviour of objects
should correspond as closely as possible to the user’s expectation of real world
objects; their behaviour; and affordances for task action.
3. Natural expression of action. The representation of the self/presence in the VE
should allow the user to act and explore in a natural manner and not restrict normal
physical actions. This design quality may be limited by the available devices. If
haptic feedback is absent, natural expression inevitably suffers.
4. Close coordination of action and representation. The representation of the self/
presence and behaviour manifest in the VE should be faithful to the user’s actions.
Response time between user movement and update of the VE display should be
less than 200 ms to avoid motion sickness problems.
5. Realistic feedback. The effect of the user’s actions on virtual world objects should
be immediately visible and conform to the laws of physics and the user’s
perceptual expectations.
6. Faithful viewpoints. The visual representation of the virtual world should map to
the user’s normal perception, and the viewpoint change by head movement should
be rendered without delay.
7. Navigation and orientation support. The users should always be able to find where
they are in the VE and return to known, preset positions. Unnatural actions such as
fly-through surfaces may help but these have to be judged in a trade-off with
naturalness (see heuristics 1 and 2).
8. Clear entry and exit points. The means of entering and exiting from a virtual world
should be clearly communicated.
9. Consistent departures. When design compromises are used they should be
consistent and clearly marked, e.g. cross-modal substitution and power actions
for navigation.
10. Support for learning. Active objects should be cued and if necessary explain
themselves to promote learning of VEs.
11. Clear turn-taking. Where system initiative is used it should be clearly signalled
and conventions established for turn-taking.
12. Sense of presence. The user’s perception of engagement and being in a ‘real’ world
should be as natural as possible.
The principles of natural engagement, natural expression of action and sense of
presence were motivated by questionnaire-based techniques for assessing the sense of
immersion in VR environments (Witmer and Singer, 1999; Slater et al., 1996). The sense
of immersion, or presence, is enhanced by a close correspondence between the VE and the
user’s experience of the equivalent real world. Compatibility with the user’s task and
domain follows the recommendations for task fit (Johnson, 1998), while heuristics 4 7
A. Sutcliffe, B. Gault / Interacting with Computers 16 (2004) 831–849 833
(close coordination, realistic feedback, viewpoints and navigation support) were
motivated by taxonomy of VR guidelines proposed by Gabbard and Hix (1997).
Heuristics 7 11 map more directly to Nielsen’s heuristics for GUI interfaces, although
consistent departures draws attention to the problem of using visual and audio feedback as
a substitute for the sense of touch. Clear turn-taking applies to conversational VEs in
which avatars may communicate with the user or when the system takes the initiative.
Underlying several of the heuristics is the assumption that the VE’s role is to represent
the real world as faithfully as possible. We contend that in the majority of VEs that is the
case (see Stanney, 2002); however, there are VEs which represent unnatural worlds, for
instance virtual molecules, or virtual information spaces. In these cases, heuristics for
naturalness need to be interpreted with reference to the fit between the user’s model of the
task and domain, and the virtual world (heuristic 2).
2.1. Evaluation method
Our method follows Nielsen’s recommendations for expert evaluation, with some
differences. Evaluators familiarise themselves with the application, carry out a set of
representative tasks, list problems encountered, then use the heuristics to interpret and
classify the problems. Heuristic evaluation depends on the evaluator being able to
establish a set of representative tasks. Since VEs are constructed to represent the real
world, user tasks should ideally mirror real world actions; however, in practice limitations
of technology mean that some compromises have to be accepted. Even when the VE
represents an artificial world such as a complex information space, the users’ ability to
move in the VE will necessitate mapping real world actions to VR technology. We have
therefore introduced an additional step to expert evaluation for VR, a technology audit that
establishes the baseline of what the VE can reasonably be expected to deliver, given the
interactive devices present in the application. The technology audit is carried out in the
familiarisation period when the evaluator explores the VE and notes the presence or
absence of features in the following categories, and any problems associated with them.
Operation of the user’s presence. The user may be represented in the virtual world by a
simple cursor or more commonly by a hand or a whole body avatar. The presence may be
controlled by a variety of devices ranging from 3D mouse, space ball, joystick to pinch
gloves and less frequently whole body immersion suits. The user’s presence and controls
can cause many problems since they provide a less than perfect rendering of the user’s
natural action. Suitability of the presence needs to be judged in relation to the user’s task.
For simple navigation, no presence may be necessary; for manipulations, however, a
virtual hand is usually necessary.
Lack of haptic feedback. True virtual prototypes have no haptic feedback (sense of
touch) so the user’s presence can pass through representations of solid objects. To mitigate
for the absence of haptic feedback, many applications use visual feedback with collision
detection algorithms to prompt users when objects are selectable or have been selected.
Problems caused by absence of haptic feedback may be observed with complex
manipulations and physical tasks. These problems can be avoided by designing augmented
reality in which interactive surfaces are modelled as physical mock-ups, but in many VEs
this is too expensive.
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Interactive techniques. Many VEs implement controls that allow users to fly through
VEs to reach and select distant objects by ray-casting. This can be taken further by
providing magic ‘snap-to’ effects so nearby objects automatically jump into the user’s
hand. These effects can cause usability problems when they are poorly designed.
Realistic graphics. VEs may not do justice to the presentation of the prototype, since
most applications are not rendered in photorealistic detail. Although some evidence
suggests that people can perform tasks naturally without detailed visual representations
(Gabbard and Hix, 1997), graphical detail will be important for information displays and
for tasks when the system environment is visually complex.
Once the technology audit has been completed, the evaluator completes a set of typical
user tasks noting any difficulties encountered. These problems are then interpreted with the
heuristics to assess the quality of presence (heuristics 1, 12 with contributions from 2 to 6)
and diagnose design features responsible for the problems encountered (heuristics 2– 11).
Problems can be associated with more than one heuristic, in which case the attribution is
assigned to the heuristic which explains the error most directly, followed by
supplementary explanations. The following checklist guides attribution of problems to
classes of design features:
Graphics display, 3D depth or perspective distortion, poor resolution of image.
Indicated by perceptual difficulties.
Moving and manipulating the user presence, sub-divided into the hardware device
being used (e.g. glove, joystick, 3D mouse, etc.) and the representation of the user in the
VE. Indicated by navigation and manipulation difficulties.
Interaction with objects and tools in the VE. Indicated by unsuccessful attempts to act;
or poor feedback misleads users.
Environmental features. Parts of the environment which created unexpected effects
such as moving through walls and floating objects.
Interaction with other controls, such as floating menus and palettes.
Other hardware problems, such as with head-mounted display (HMD) and shutter
Once the evaluator has diagnosed the problems by assigning them to heuristics and
design features as far as possible, the final stage is to rank the severity of the errors by
heuristic. Indications of the severity of the identified problems are given, ranging from
poor design with a severe impact likely to result in task failure to a minor problem
probably curable by training. This ranking reflects the number of errors assigned to each
heuristic with the evaluator’s judgment about the severity of those errors, on a four-point
Severe. The problem encountered would make it impossible to complete the task
Annoying. The problem would disrupt the user’s task but most users would learn how to
cure the error given an explanation, and some might find a work-around with time.
Distracting. The problem would disrupt the user’s tasks but most users would discover
the fix relatively quickly given a hint.
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Inconvenient. The problem could disrupt the user’s task but most users would discover
the fix unaided.
The rankings provide a summative evaluation of the VE as well as a formative
evaluation by prioritising areas for redesign in the next version, following the normal
practice of heuristic evaluation (Nielsen, 1993).
3. Case studies
Two case studies were carried out to test the effectiveness of the VE heuristic
evaluation method. The evaluator in these studies has 4 years experience in HCI research
including a PhD in evaluation methodology, so he could be classified as an HCI expert.
The evaluator had not used the VE heuristics before this study.
3.1. Evaluation of a scene of crime VE
The VE application used in this study was a fully immersive application using a HMD
and hand-held interactive 3D mouse. It depicted a 3D environment of a scene of crime
committed in garage premises and was developed by the REVEAL project in the
University of Manchester (Hubbold et al., 1999). The user is able to explore the garage
layout and thereby gather information about the scene of the crime. At several points
within the VE application, pictures from the real environment have been inserted to help
investigation of crime scenes. The representative task was to locate and enter the office in
the garage premises in which the crime had been committed and note details which might
be important for the prosecution, e.g. traces of blood, open window, disturbed objects, etc.
The task involved the evaluator walking through the VE several times, inspecting details
in the side room adjoining the garage, and finding the photographs embedded in the VE
(see Fig. 1).
3.1.1. Technology audit
The VE controls were inspected during the familiarisation period when the expert
explored the application to produce the following technology audit:
Operation of the user’s presence. The user’s presence was minimal since the user’s
movement and viewpoint controlled all the displayed area. The exogenous viewpoint
with no user presence was sufficient for navigation and inspection tasks; however, an
avatar presence might be needed if the user wanted to investigate physical movements,
such as exit through a small window.
Haptic feedback. Since the application did not require manipulation of objects, no
haptic feedback was necessary, although it would be if objects in the VE were moved
and investigated.
Interactive techniques. The restricted size of the VE and lack of interactive objects
meant no controls or interactive facilities were necessary.
A. Sutcliffe, B. Gault / Interacting with Computers 16 (2004) 831–849836
Realistic graphics. Since the application demanded inspection of detail, high fidelity
graphics were required.
Interactive support in the VE was clearly limited, so problems needed to be interpreted
in the perspective of a simple navigate and explore environment. The VE was explored
thoroughly and details inspected as implied by the task. Six major problems were
Fig. 1. Screen shot of the scene of crime VR showing (a) plan overview of the VE, and (b) the garage layout with
an embedded scene of crime photograph.
A. Sutcliffe, B. Gault / Interacting with Computers 16 (2004) 831–849 837
encountered leading to the analysis of usability problems with reference to the heuristics,
as shown in Table 1.
Most of the problems encountered were caused by poor navigation support and the
technological limitation of delays in updating the 3D graphics display when moving and
changing viewpoint in the VE. The attribution of the problems and severity rating is given
in Table 2. The potential problems are also classified as requirements where clarification
Table 1
Heuristics rating and interpretation of problems encountered
Heuristic Rating Problems encountered
1. Natural engagement 3 The VE maintains a fair degree of realism, but
many contents appear to float. Photographs of
the real environment make the VE appear
less natural
2. Compatibility with the user’s task 3 Being able to fly through the air and being able
to pass through solid objects, e.g. walls
and the floor, not expected
3. Natural expression of action 2 Interaction with objects to explore the scene,
e.g. moving furniture, was not possible
4. Close coordination 3 Some graphics rendering delays interfered with
engagement during navigation. Also the slow
constant speed of navigation was somewhat
5. Realistic feedback N/A No interaction with objects supported
6. Faithful viewpoints 4 Generally good although some problems noted
under heuristic 4
7. Navigation and orientation support 3 Ability to walk through walls caused
disorientation; slow pace annoying
8. Clear entry and exit points N/A Not relevant for immersive VR apart from
changed environments, exit from VE in desktop VR
9. Consistent departures 3 Realistic photos incongruent
10. Support for learning N/A Not necessary for simple navigation
11. Clear turn-taking N/A Single user VE, no avatars
12. Sense of presence 4 Reduced by jerkiness in display and contrast between
photos and VE graphics; see heuristics 1, 4 and 7
Table 2
Classification of problems encountered with severity ratings and suggested design improvements
Feature Problem description Problem rating Design change
Graphics Rendering delays,
floating objects
Inconvenient Faster hardware
Presence N/A
Interaction Explore objects Distracting Requirement clarification
Pass through surfaces Annoying Software: add movement constraints
Controls Incongruent photos Distracting Software: provide ^photo controls
Hardware N/A
A. Sutcliffe, B. Gault / Interacting with Computers 16 (2004) 831–849838
was needed from the user; software problems which could be rectified by the VE
application designer; and technology problems beyond the designer’s control, e.g.
The graphics rendering problems were an inconvenience which could be cured by
running the application on a faster machine or improving the rendering algorithm. Floating
objects were a perceptual distraction caused by poor shadowing. The interaction problem
of not being able to move objects to explore the VE depended on interpretation of the user’s
requirements. If the VE was intended to support further investigation, then there was a case
for supporting such interaction; however, this raises a scope of modelling problem: how
many objects should be movable and which hidden objects should then be visible? The user
specification has to indicate the extent of the real world to be modelled in the VE, and this
will depend on the extent of knowledge of the crime when modelling occurs. Given this
limitation, a requirement for a less interactive aide-memoire system seems to be
acceptable. The disorientation problems encountered when passing through walls and other
surfaces could be avoided by adding movement constraints on the user’s presence,
although some (audio) feedback might be advisable to signal this limitation to the user.
3.2. Evaluation of chess game VE
The second VE application was a chess game developed in a fully immersive CAVE
system (Cruz-Neira et al., 1992, 1993) equipped with shutter glasses to give users
stereoscopic views, and pinch gloves for manipulation. Head-tracking devices mounted on
the users’ shutter glasses controlled the CAVE viewpoint according to the users’ body and
head movements and corresponding devices tracked the users’ hands.
The application displayed 12 chess pieces on a board with minimal background (see
Fig. 2). The user’s tasks were to move the chess pieces from a random layout to the target
arrangement shown in the CAVE display. Haptic feedback for piece selection was
substituted by colour changes so when a user operated the pinch glove to select a chess
Fig. 2. Selection of a chess piece.
A. Sutcliffe, B. Gault / Interacting with Computers 16 (2004) 831–849 839
piece, the piece changed colour (from white/black to yellow) in response to the user’s
action (Fig. 2). The selected piece then moved in tandem with the user’s hand until it was
released. Once the piece was released it reverted to its original colour.
When an already selected chess piece (coloured yellow) was correctly positioned to be
gripped by the other hand it changed colour from yellow to blue. The user released the
piece with their first hand and the chess piece colour changed back to yellow, indicating
that it continued to be selected by the second hand (Fig. 3).
The representative task was arranging the initially scattered chess pieces into an
ordered arrangement with black and white pieces placed on their correct starting positions
for a chess match. This was initially performed by single-handed interaction and then
repeated by passing a piece from hand to hand, to evaluate more complex manipulation.
The tasks were completed under two different conditions: (i) VE application with the
virtual hand present; and (ii) VE application without the virtual hand being present. In both
conditions, a precise bounding box provided collision detection when the virtual hand
intersected with the surrounding volume of each chess piece. When the virtual hand was
not represented it was necessary for the user to infer the offset between the real hand and
its virtual position in the CAVE.
3.2.1. Technology audit
Operation of the VE was investigated during a learning period, resulting in the
following technology audit:
Operation of the user’s presence. The user’s presence was a virtual hand; however, it
did not show any effects of pinch movements so this limited visual feedback. Only
thumb and index finger pinch operations were supported.
Lack of haptic feedback. Haptic feedback was substituted by colour changes in the
chess pieces to indicate they were selectable. Colour changes were triggered by
a collision detection algorithm when the user’s virtual hand was close to the piece.
Fig. 3. Passing a chess piece from hand to hand.
A. Sutcliffe, B. Gault / Interacting with Computers 16 (2004) 831–849840
Interactive techniques. The interactive objects were manipulated by pinch operation
and moved by hand/arm or body movements. The position of the user hand and
head/body were tracked independently by sonic devices. The small size of the VE did
not require any navigation support.
Realistic graphics. The application had a very sparse representation of the chess pieces
and minimal environment.
The VE was clearly limited by the lack of haptic feedback for interactive support, so
grip and manipulation problems needed to be interpreted in this perspective.
Carrying out the task of picking up, passing and replacing all the pieces (five pieces for
each colour) produced a list of seven major usability problems, which were subject to
heuristic analysis, as shown in Table 3.
Most of the problems encountered were caused by learning the rules for manipulating
the chess pieces with the colour changes substituting for the lack of haptic feedback. The
classification of user problems is given in Table 4.
Graphics problems occasionally caused attention to be distracted by distortion effects
within corners of the CAVE, but such problems are known limitations of room- or cube-
shaped CAVE environments and can only be cured by CAVE display domes. Graphics
rendering delays can be cured by improved hardware or faster rendering algorithms. When
the virtual hand was absent perceptual problems were much worse because the position of
the virtual hand in the 3D graphic space could not be directly mapped to the user’s visible
real hand. Problems encountered with use of the pinch glove to manipulate objects all
emanated from use of colour changes that substituted for absence of haptic feedback.
Table 3
Heuristics rating and problems encountered
Heuristic Rating Problems encountered
1. Natural engagement 3 Possible to pass the chess piece through the chessboard;
passing pieces between hands difficult; display distortion
2. Compatibility with
the user’s task
2 Unclear colour changes when passing chess
pieces from hand to hand
3. Natural expression of action 3 Lack of haptic feedback meant colour changes and glove
pinch actions had to be learned; see also heuristics 1 and 2
4. Close coordination 4 Intermittent graphics rendering delays somewhat annoying
5. Realistic feedback 3 Colour changes substituted for haptic feedback were not clear
6. Faithful viewpoints 4 Generally good although could move out of the VE world
7. Navigation and
orientation support
4 Ability to walk through walls caused minor dissonance
8. Clear entry and exit points N/A Step out of CAVE
9. Consistent departures 0 No change in initiative. Colour changes for chess
piece manipulation were consistent
10. Support for learning 4 Consistent visual cues helped learning of object
11. Clear turn-taking N/A Single user VE, no avatars
12. Sense of presence 4 Reduced by some rendering delays and sparsity
of overall display; see also heuristics 1 and 2
A. Sutcliffe, B. Gault / Interacting with Computers 16 (2004) 831–849 841
The colour changes needed some improvement; however, the glove pinch operations were
reasonably naturally given the technological limitations. Absence of the virtual hand made
interaction problems worse because the user had to learn the offset between their visible
real hand and the invisible bounding box that represented their presence. Distortion in 3D
depth made this learning process difficult. The ability to pass through surfaces caused
some dissonance and this could be cured by adding movement constraints, although these
may have to be made explicit to the user by audio feedback. Overall, this VE received a
reasonably favourable evaluation given the limitations of the available technology.
4. Assessment with several evaluators
The VE application used in this study was the same as the previous case study;
however, the application designer had taken the results of the first evaluation into
account and changed the visual cues. When a piece was placed on the chessboard, the
square on which it was placed changed colour from white/blue to dark red (see Fig. 4).
When the user released the piece, the square reverted to its original colour. Also, a
movement constraint was introduced to prevent chess pieces from being able to pass
through the chessboard.
Seven undergraduate students (6 males, 1 female) from UMIST took part in the study.
All were taking the advanced HCI course so they had knowledge of evaluation techniques
and Nielsen’s heuristics; however, they had only been introduced to the VR heuristics 1
week before the experiment and they had no prior knowledge or familiarity with VE
applications. The evaluators were asked to complete the same task in the CAVE and
follow the method using the 12 heuristics listed earlier. However, they did not test with the
no-virtual hand condition and did not complete the technology audit phase of the method,
although the results of the audit were presented to them to help interpretation of problems.
The task required evaluators to pick, move, pass, and replace a single chess piece on the
chessboard. The initial list of problems encountered were:
Perceptual depth difficult to judge in places.
Problems in placement phase of chess piece manipulation.
Table 4
Classification of problems encountered with severity ratings and suggested design improvements
Teature type Description Problem rating Design change
Graphics Rendering delays,
display distortion
Inconvenient Faster hardware
Presence Grip, manipulation problems Annoying Software: improve colour changes
Interaction Manipulating object Distracting Hardware: provide haptic feedback
Pass though surfaces Annoying Software: add movement constraints
Controls N/A
Hardware N/A
A. Sutcliffe, B. Gault / Interacting with Computers 16 (2004) 831–849842
Problems observed when passing chess piece from hand to hand, i.e. difficult to see
colour change.
General jerkiness and intermittent delay in graphics rendering.
Absence of haptic feedback.
The evaluators in this study also identified problems with the application environment,
where typical comments included: ‘The graphics are a bit distorted’, ‘Graphics are blurry’,
and ‘Slow update’. Problems were also experienced in the use of the pinch gloves, where
typical comments included: ‘Pick up hard’, ‘Lag on hands was bad’, and ‘Found it difficult
to place piece on board’.
On the completion of the task, each evaluator rated the usability of the VE for each of
the 12 heuristics. The same procedure was used as before except the four-point scale was
substituted with a 1 (very poor) to 7 (very good) point scale to increase the discrimination
in the evaluators’ judgement. They were asked to report the reasons for their decisions and
any interaction problems they had observed under the relevant heuristic. These rating
scores were converted into net positive values (NPV) to reflect the range of the users’
assessments. A worked example of this analysis, converting a 1 –7 to a 23toþ3 scale, is
given in Table 5.
Fig. 4. Placing a chess piece on the chessboard, after design modifications.
Table 5
Worked example of evaluators’ net positive value rating of heuristic 1 for the VE application
Rating scale 1 2 3 4 5 6 7
Conversion scale 2322210123
Rating frequency (7 evaluators) 0 1 2 4 0 0 0
Product 0 22220000
Total net positive value (NPV) ¼24
A. Sutcliffe, B. Gault / Interacting with Computers 16 (2004) 831–849 843
The evaluators’ ratings of the VE application using the 12 heuristics is given in Table 6.
These show moderate variation with most ratings tending towards poor or neutral
assessments. An exception to this is the relatively high NPV rating scores for heuristic 6
(faithful viewpoints) and heuristic10 (support for learning). For heuristic 6, the evaluators’
rating score (6) agreed with the experts’ judgement in the previous study, although they
disagreed on heuristic 10. The VE application only had minimal visual cues and in
addition, as cited under heuristics 2 and 5, it was possible to pass a chess piece through the
chessboard, since no movement constraint was provided.
The evaluators’ mean ratings and the single expert’s rating (see Table 3) are similar, but
did show some improvement between the evaluations. This indicates that the evaluation
and the changes made by the application designer had been successful. This was reflected
in comments on one of the design features which had been changed, insertion of additional
visual cues: ‘Visual cues are good” and ‘Use of colour makes up for lack of haptic sense’.
Furthermore, the NPV rating score for heuristic 3 (natural expression of action) was
positive. The addition of more evaluators did not reveal more usability problems but this
may have been limited by the simple nature of the application.
The evaluators’ judgement for most of the heuristics was generally consistent as the
distributions of scores on all heuristics had normal distributions (i.e. non-bipolar),
although the low overall number of evaluators precluded statistical testing of inter-
evaluator agreement. Four of the seven evaluators gave no rating for heuristic 8 (clear
entry and exit points) and three evaluators gave no rating for heuristic 11 (clear turn-
taking), which they considered to be not applicable. All the evaluators reported and
analysed three out of the five general problems but two evaluators did not report perceptual
depth and three did not comment on the absence of haptic feedback. However, when the
evaluators’ judgement was compared with an independent evaluation with users on the
same application (Sutcliffe et al., in prep.), the granularity of the problem description in
this study was not as precise as problems discovered by observing users’ errors.
Table 6
NPV ratings: means and ranges (on 1 –7 scale) for the 7 evaluators’ scores for the VE application, where 1 ¼poor
quality and 7 ¼excellent quality for that heuristic
Heuristic NPV Mean Range
1. Natural engagement 24 3.4 2–4
2. Compatibility with the user’s task 24 3.4 2–5
3. Natural expression of action 3 4.4 3 5
4. Close coordination 22 4.3 2–7
5. Realistic feedback 23 4.0 1–6
6. Faithful viewpoints 6 4.3 2–6
7. Navigation and orientation support 2 4.3 1 7
8. Clear entry and exit points 1 4.3 0 5
9. Consistent departures 1 4.1 3 5
10. Support for learning 9 5.3 3 7
11. Clear turn-taking 3 4.8 0 6
12. Sense of presence 23 3.6 3– 4
NPVs provide an aggregate score rating to interpret mean and ranges.
A. Sutcliffe, B. Gault / Interacting with Computers 16 (2004) 831–849844
For instance, users noted problems in selecting the chess piece caused by the collision
detection algorithm, as well as in passing pieces. In common with other studies on
heuristic evaluation, more than five evaluators trapped most of the serious errors; however,
the heuristics did not clearly indicate the root cause of the problems.
4.1. Evaluation of the heuristics
Finally, each evaluator was asked to rate the 12 heuristics on a 1 7 scale as to how
applicable/valid they considered each heuristic to have been in their evaluation, and the
reasons for their decision. The evaluators’ NPV ratings given in Table 7 show a moderate
variation with most ratings being favourable and therefore applicable/valid assessment.
However, low NPV scores were given for heuristics 8 (clear entry and exit points) and 11
(clear turn-taking), with moderate rankings for heuristics 7 (navigation and orientation
support) and 9 (consistency departures). For heuristic 7, the evaluators’ rating score (6)
agreed with the experts’ view on the heuristic’s applicability. Evaluators’ comments
included: ‘Not necessarily appropriate, if in a simple single environment’. For heuristic 8,
the evaluators’ rating score (27) was low; however, this was explained by their comments
on the heuristic’s applicability. Four of the seven evaluators considered the heuristic to be
‘not applicable’; typical comments included: “There wasn’t really the case because we just
had to put the gloves and glasses on”.
Two evaluators questioned the meaning of heuristic 9. Finally, for heuristic 11, the
evaluators’ rating score (1) was low because of its inapplicability, three of the seven
evaluators considering it to be ‘not applicable’.
4.2. Lessons learned
In the initial single expert study, the heuristics proved to be easy to interpret and
indicated problem areas in the design; however, heuristics 8 and 9 (entry/exit points,
consistent departures) were more applicable to desktop VR and applications with
Table 7
NPV ratings for the utility of the heuristics by the seven evaluators
Heuristic NPV
1. Natural engagement 15
2. Compatibility with the user’s task 16
3. Natural expression of action 13
4. Close coordination 16
5. Realistic feedback 15
6. Faithful viewpoints 17
7. Navigation and orientation support 6
8. Clear entry and exit points 27
9. Consistent departures 7
10. Support for learning 13
11. Clear turn-taking 1
12. Sense of presence 11
23, not useful and þ3, very useful for interpreting usability.
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embedded GUI features. This suggested that the heuristics needed to be filtered at the
technology audit stage so only relevant ones were applied. There was some overlap
between the first three heuristics, which all referred to slightly different aspects of natural
In the several evaluators study, the need to filter the applicability of the heuristics was
also evident. Tailoring the heuristics to different types of VE will cure the ‘not applicable’
problem. The heuristics need to be presented as a core set for all VEs (1 –7 and 12) with the
addition of heuristics 8 (clear entry and exit points) and 9 (consistent departures) for
desktop VR, and heuristic 11 (clear turn-taking) for collaborative VEs or when system
initiative is involved. The importance of navigation support (heuristic 7) and support for
learning (heuristic 10) will depend on the complexity and size of the VE. Some
clarification of heuristics 1 3 will help to separate issues of overall experience of
engagement (1), compatibility with users’ expectations (2) and controllability of actions
(3). Several evaluators reported difficulty in interpreting heuristics. This could be
ameliorated by giving examples of good and bad VEs to illustrate each heuristic; however,
examples might bias users towards irrelevant details. On balance, we feel increased
training is the answer to interpretation, rather than provision of limited examples. Finally,
there is the question whether we need additional heuristics for problems and design issues
we may have omitted. The comments of our evaluators and the problems they encountered
suggest that our current set is appropriate, although experience with developing
technology may require enhancement of the set in the future.
The advantages of the heuristics were that they are quick to use and provide insight into
usability problems by drawing attention to high-level design concerns. This role is shared
with other heuristics which have been proposed for evaluating different types of user
interfaces, e.g. CSCW applications (Baker et al., 2002), ambient displays (Mankoff et al.,
2003). The limitations inevitably are the trade-off between rapid use and detailed advice,
which can be found in taxonomies of guidelines (Gabbard and Hix, 1997). One
improvement may be to add heuristics that focus attention on the major components of
most VEs, i.e. the user’s presence, interactive objects, and the quality of the graphical
5. Discussion and conclusions
The case studies demonstrated that the 12 heuristics we developed from earlier work
(Sutcliffe and Kaur, 1997) provided a useful tool that performed an efficient and
meaningful usability evaluation of VE applications. The heuristics augment the criteria for
evaluating desktop VR proposed by Johnson (1998): task fit, navigation support and
subjective satisfaction, while providing a quicker evaluation process than the VRUSE
usability feature checklist (Kalawsky, 1999). Of the 10 usability factors in VRUSE, input
and output devices map to the technology audit in our method, while others (error
correction, consistency, user guidance) are similar to Nielsen’s UI heuristics; while only
two (simulation fidelity and presence) are explicitly targeted at VR applications. Overall,
in the context of the three studies, we consider the heuristic evaluation process to have
been a success since usability problems were identified with only a small expenditure of
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effort. The heuristics represent an important extension to expert HCI evaluation methods
and address the specific issues raised by VEs, in particular, the integration of presence and
usability. However, we believe it is important to judge VEs from a baseline of the
available technology, hence the audit part of the method played an important role. The
method we proposed combines heuristics with a technology audit that focused on
particular aspects of VR technology. We argue that since VR aspires to create the perfect
illusion of an interactive world, but is inevitably limited by the technology for rendering
interaction, it is important to benchmark the evaluation by making the limitations of the
technology explicit.
In contrast to studies on Nielsen’s heuristics, which demonstrated that more experts (up
to six) trapped more errors within a law of diminishing returns, we did not find increasing
the number of evaluators discovered many more errors. However, this might have been
caused by a ceiling effect with a simple application, i.e. there were no more problems to
find. As our evaluators were arguably novices, it is reassuring that they found the same
errors as the single expert. This demonstrates that the method can be used by evaluators
who have limited HCI training, a finding which agrees with studies on UI heuristics
(Nielsen and Molich, 1990). The small number of errors in the VEs we studied may have
disguised the effort of adding more evaluators to trap additional errors. Furthermore, many
of the errors had the same root cause, i.e. problems in selecting and placing pieces could be
traced to depth perception and the collision detection algorithm which signalled when a
piece was selectable. Problems can be reported at different levels of abstraction and this
can limit the effectiveness of heuristic analysis.
The approach we have adopted is similar to other extensions to Nielsen’s method for
CSCW (Baker et al., 2002) and ambient displays (Mankoff et al., 2003). These authors also
modified Nielsen’s basic set to include more abstract qualities of the design, for instance
‘sufficient information design’ for ambient displays (Mankoff et al., 2003) and ‘provide
consequential communication of an individual’s embodiment’ in CSCW (Baker et al.,
2002). Mankoff et al.’s study demonstrated that their specialised heuristics were superior
to Nielsen’s in analysing errors. While we do not have comparative data, we expect our
heuristics to be superior to Nielsen’s for VEs; however, we believe that a combination of
standard Nielsen and specialised heuristics may produce the optimal result. The heuristic
method presented in this paper augmented Nielsen’s approach by adding the technology
audit which we argue is an important way of calibrating judgement for different
technologies. The method is a generic tool for the evaluation of VE applications; however,
it needs to be tailored to different styles of VE. For instance, desktop VEs frequently
employed part of a GUI interface, so heuristic 8 (clear exit and entry points) is relevant,
whereas for immersive VE it is not. Likewise, clear turn-taking only applies when other
actors are present in the VE. In our revised method, we have included a guide to filtering
the heuristics, so only appropriate questions are asked. We expect our heuristics to be one
in a battery of evaluation techniques, which can be employed according to different
resource constraints; for example, cooperative evaluation by observing users’ problems
(Monk et al., 1993). Observation of users’ problems and interpretation of error causes has
been demonstrated for immersive applications (Hix et al., 1999). Questionnaires will
continue to play a role in summative evaluation of presence (Witmer and Singer, 1999;
Slater et al., 1995), and expanding the heuristics we proposed could be used for summative
A. Sutcliffe, B. Gault / Interacting with Computers 16 (2004) 831–849 847
evaluation with these techniques. Finally, cognitive walkthrough approaches have also
been demonstrated for VR using an extension of Norman’s model of action for VR (Kaur
et al., 1999). Bowman’s framework for comparing evaluation methods for VR applications
(Bowman et al., 2002) provides a means of locating the contribution of our heuristic
method in a wider perspective of other methods, although further studies will be necessary
to develop a means of selecting the optimal approach to adopt given a set of evaluation
needs and resource constraints.
We would like to thank Adrian West, Toby Howard and Roger Hubbold of the
Advanced Interfaces Group, Department of Computer Science, University of Manchester
for the use of the application used in the first case study. We would also like to thank
Terrence Fernando and Kevin Tan of the Centre for Virtual Environments, University of
Salford, for their assistance in the latter two case studies, and students from the
Department of Computation, UMIST for their participation. The research was supported
by EPSRC grant GRM68749 Immersive Scenario-based Requirements Engineering.
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Heuristic evaluation is a famous usability inspection method often used in the assessment of Augmented Reality (AR) systems. Usability heuristics can be characterized as general or specific to a domain. During comparative studies between a new and a traditional set of heuristics, the focus is often around performance metrics (number of problems, time, errors), and little attention is given to the experience itself (i.e., evaluator’s overall satisfaction toward utilization of the set of heuristics). In addition, limited research has been done on the application of heuristics to evaluate interfaces with different levels of interaction, along with how a longer set of low-level heuristics compares against a traditionally-sized set of high-level heuristics. In this study, recruited participants (mostly university students) performed a heuristic evaluation using either a set of 110 specific heuristics (SH) or a set of 23 general heuristics (GH) to evaluate two versions of an AR application, designed with a low and a high level of interactivity. Big Five personality traits, prior experience with AR or Virtual Reality systems, and various usability data (e.g., subjective ratings related to the usability of heuristics and AR application, number of unique problems, and severity ratings) were collected through questionnaires and utilized during statistical analysis. Results show that even though the employment of the set of GH produced higher overall satisfaction of evaluators, the set of SH resulted in a significantly larger number of unique usability problems. Furthermore, the more interactive version of the AR application was perceived by evaluators as the harder version to inspect, regardless of the set of heuristics utilized. Neuroticism and agreeableness traits were found with a significant impact on subjective usability ratings.
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This research aims to provide a better understanding of the effectiveness of existing virtual tours (VT) to induce a favorable attitude toward Alexandria"s heritage sites, through the study of the VT of the three Roman sites in Alexandria, and assessing the user"s sense of presence, experience and their effect on the attitude change towards the visited archaeological sites and towards the Grӕco-Roman archaeology in general. To collect the required data a self-administered online survey was used. The sampling frame included undergraduate students in the Faculty of Hotels and Tourism, Alexandria University. Three identical questionnaires were designed, investigating the research variables. Participants were asked to answer the questionnaire after performing a VT to the designated site provided to them by a link of the website. A quantitative approach was used to examine the validation of the study hypotheses; SPSS V. 24 was used for data processing. The study results supported the study hypotheses, showing that both presence and experience of the VT had an obvious effect on attitude of the visitors towards the visited heritage sites. Despite the increasing importance of the VT of heritage sites, there are few recent studies which investigate the role of these VT on attitude change of the visitors. None of these studies assessed the factors that affect their attitude particularly in the Grӕco-Roman sites of Alexandria. Thus, this study gives an insight on the effect of both presence and experience of VT on attitude of the visitors.