ChapterPDF Available

Designing for Expertise

Product Experience
Copyright © 2007 Elsevier Ltd.
University of Washington, Seattle, WA, USA
The Ohio State University, Columbus, OH, USA
Designers are often surprised when innovative new systems are introduced to the fi eld.
Suddenly, the new products or systems encounter people, and with them practices and
operations that have been established before the design of a new product was envi-
sioned. As the new designs are fi elded, users are confronted with the new products and
wonder how they afford new possibilities and how they can be integrated into current
How artifacts are put to use depends in large part on the perspectives, initiatives, and
inquiries of these users. The people who will engage in the application of the new prod-
ucts are in many instances more than users; they are practitioners of trained skills. They
are professionals who contribute their knowledge to the application of the new products
and systems as they utilize these in order to accomplish their goals. Practitioners utilize
products and systems with purposes in mind. Do the practitioners consider a product
useful? Can they use it to advance their goals? Does it make sense to them in terms of
how it infl uences or changes their activity?
As designers, we have learned to study design challenges from the perspectives of the
people who will put the new designs to use: How do practitioners know what to do with
the new design? What are the intentions and judgments that guide operations? How do
practitioners anticipate what will likely happen next as they engage in a particular oper-
ation with a new product? How can we – as designers – design artifacts and systems that
support practitioners, so that they excel in what they are already good at? How can we
integrate technologies by design to support the expertise of others or to help create new
forms of expert activity?
Understanding how practitioners make decisions and formulate plans for action is a
prerequisite for designing systems that facilitate domain expertise in context. We will illus-
trate in this chapter that expertise is comprised of many factors, and relies on the train-
ing and experience of practitioners in their domain. Expertise represents a convergence of
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knowledge, skills, and experience that results in competence – the ability to make appro-
priate decisions and assessments with regard to the situations at hand.
Experts operate in a variety of domains. We fi nd them in serious domains where
they act in high-stakes functions as surgeons, pilots, judges, commanders, and high-level
decision makers. Many of us are experts in everyday environments, where we converge
special understanding and particular skills as technicians, designers, cooks, musicians,
wine connoisseurs, and athletes (Hoffman, 1992).
Can just anyone who is particularly knowledgeable in a specifi c domain of human
activity be considered an expert by others who depend on the consequences of these deci-
sions or actions? On a day-to-day basis, we use the label ‘expert’ both as an indicator
for a high level of profi ciency, training, and knowledge that is generally associated with
extended practical experience, and as a social construct, where a group of stakeholders
identify those who are more expert among them.
As more radical innovations in the form of technology-intense systems, software-
driven products and digital services continue to transform our daily lives, being expert
has acquired a new meaning. Those among us who manage to cope with novelty can
learn and adapt to be able to catch up with the continuous series of iterations, updates
and improvements in a continuously changing environment. In contrast we often fi nd lab
researchers and developers speaking of novice and expert users; in other words, acting as
if one could divide the audience of new technological artifacts into those who are in the
know, and those who do not know. This has always been a distortion as expertise is tied
to a context and depends on more than just cumulative experience.
What is it that experts know and how is it relevant to design? Expertise is rooted
in a particularly deep-level understanding of a domain of practice. This understand-
ing utilizes explanations and reasoning strategies that are extracted from a conceptual
model of the domain, constructed and continuously consulted, tested, and updated by
the domain expert in the course of practicing in the domain by engaging in activities in
order to achieve goals. The conceptual model takes the form of an abstract representa-
tion (Rappaport, 1997; Heiser and Tversky, 2005).
Briefl y, a conceptual model is composed of functional and physical relationships in
the domain that were identifi ed by the expert and that allow him or her to simulate what
is likely to happen next (Klein and Crandall, 1995). The conceptual model assists the
expert in fi nding explanations for observations. The expert has constructed this concep-
tual model in the course of exploring the domain along various storylines and getting
feedback about the different events that follow in various situations. Conceptual models
as mental representations serve as structures for reasoning strategies that utilize actual
observations in the course of sense-making (Klein et al., 2006).
Design infl uences the acquisition of these mental conceptual models by provid-
ing external representations that infl uence how people see the world and obtain feed-
back about the world. Design infl uences the use of these mental conceptual models by
changing the mental workload (memory and attentional loads) associated with making
sense of situations and planning how to act. These external representations mediate the
user’s cognitive work to develop and use conceptual models to make sense of the world
accessed through the external devices and displays.
Research on expertise is concerned with issues that arise in the course of work with
representations in real settings – where people trained to different levels of profi ciency
work at tasks to different levels of skill. We will, in the following, focus on factors that
determine the knowledge and performance of experts – in roles as operators of artifacts
and systems, and as stakeholders that face the complexities posed by novel technology.
Expert operators and decision-makers form intentions – what needs to accomplished,
216 Product Experience
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Designing for expertise 217
transform these intentions into plans and actions, recognize disruptions to these plans,
cope with complexities and adapt their activities to achieve goals.
Note that we replace the common terminology of ‘user’ with the label practitioner.
We use the latter term to emphasize that people are not passive recipients of products
designed for them or passive rule followers. Au contraire – people actively make things
into tools and adapt plans to achieve goals as conditions and environments change.
Practitioners will modify unsatisfactory design, devise workarounds, or simply abandon
artifacts that do not enable their purposes.
Our objective as designers is to support the work of practitioners in context by uti-
lizing innovative technologies that are useful, usable, and easily understood. Only if
designers understand the role and nature of expertise can this integration be possible and
successful (Ericsson et al., 2006).
The designer’s focus is on how technological novelty can affect people. By envisioning
how new technologies could take shape in the form of artifacts, systems and environ-
ments, designers mediate technological possibilities with what future practitioners iden-
tify as useful, useable, and understandable. As human-centered design, this perspective
on design practice dates back to the formation of work studies in human factors engi-
neering and industrial psychology after World War II (Dreyfus, 1955).
The initial application of human-centered design techniques in aviation, in indus-
trial manufacturing, and on the battlefi eld illustrates that the focus of the practitioner-
centered view in design has originated in domains where things can go wrong with serious
consequences when designers have solved the wrong problems even when using the right
techniques (Woods et al., 1994, chapter 5). To avoid these design failures, designers have
learned to study the prospective users of the proposed design: Who are these practitioners,
what are their expectations, and what do they know?
As we move into more advanced work domains that encompass considerably com-
plex technological environments, we deal with highly trained and knowledgeable practi-
tioners who perform as experts in their domain. In order to design from their perspective,
we need to understand what identifi es their expertise. Plus, technology advances are mak-
ing more everyday activities considerably complex environments that place increasing
demands on people’s expertise – often expertise in working with poor interface designs.
Common to all design process models is that they demand that designers understand
the domain of human activity before they can be able to integrate useful design inno-
vation into existing contexts (Bayazit, 2004). The question is: If the domain relies on
the performance of expert practitioners, do the designers have to become experts in that
domain before they can understand the needs of practitioners and thus create appropri-
ate design support? On one hand it simply requires too much time to become a domain
expert. But besides the time constraint, it is quite ambitious for a designer to presume
that he or she could become an expert surgeon, a pilot, or an intelligence analyst at
all. Such achievement could take long training periods, extended apprenticeships, and
extensive experience with diffi cult situations. And expertise at design may have little in
common with the kinds of knowledge or skills that experts possess in other domains of
human activity. What does it take to become an expert? Is there a fast lane for design-
ers to understand the expertise of practitioners so that they can apply human-centered
design techniques that do not fall prey to oversimplifi cations about what experts in
different domains do?
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218 Product Experience
Being an expert, on the other hand, can hinder design work where it comes to re-
thinking existing products. The expert might not be able to see why a routine activity
from his or her point of view may pose a challenge to practitioners who perform at dif-
ferent levels of profi ciency. Expert may be biased by how things are done today when
evaluating diverse alternative strategies and design concepts that would change how
things would be done tomorrow. Doing design work in expert domains, designers face
the Ethnographer’s Challenge: in order to make interesting observations, they have to
be, in part, insiders in the setting they are observing, while remaining, in part, outside
the domain in order to have insights about how practice works, how practice fails and
how it could work better given future change. Design observations in the fi eld of prac-
tice, where designers watch experts doing cognitive work, relies on being prepared to be
surprised in order to distinguish unexpected behaviors that reveal how expertise works
and how these experts work (Woods and Hollnagel, 2006).
We have encountered examples where user analyses in interaction design contexts
mystify rather than clarify domain expertise. It is very diffi cult to characterize expertise
of people in the cost- and time-pressured setting of usability testing environments. It is
easy to oversimplify the role of expertise, especially when there are limits on access or
high costs to contact with actual practitioners. For example, the need for rapid design
input might lead to situations where one might ask software specialists to evaluate the
usability of a piece of software designed by other software experts.
Dangers like this often lead to the formation of interdisciplinary design teams. But
simply juxtaposing specialists from different backgrounds does not make an expert
design team. When working in such teams on products for experts, designers in interac-
tion design, industrial design, and visual communication design often feel unprepared to
assess complexity in technical domains. They may even feel intimidated by the excessive
detail in specifi cations and the complex nature of underlying processes. Visual design-
ers often state that good solutions need to be simple solutions. Systems engineers, on
the other hand know that only complexity can cope with complexity; that often, there
cannot be a simple solution to a complex problem – the problem is complex because
an appropriate response can take many forms. Confronting the open possibilities and
the critical uncertainties, designers draw, build and explore where in contrast engineers
tend to simulate, analyze, and plot results. Both model but in completely different ways.
Design builds tangible models people can play off as they envision future possibilities;
engineers model how processes work fundamentally in order to run simulations that
would predict exactly how things would work under postulated different, that is, future,
conditions. For the former, design is discovering new possibilities (and this requires gen-
erating a wide range of novel approaches and combinations); for the latter, design is
applying a set of constraints on what is a solution to narrow in on a solution provided
directly once one has the results from the right series of analyses or simulations.
Working only besides each other these two perspectives are both needed and are both
incomplete. Simplicity cannot be designed without confronting complexity (e.g. there are
limits to using techniques like out-of-the-box testing where the designer assumes the role
of the outsider, the non-specialist, and the novice that walks up to an artifact and is
assumed to operate it without prior instruction). Analysis alone cannot guide how peo-
ple adapt new capabilities into real tools and in this process transform roles, processes,
decisions, diffi culties and even what is expertise.
One challenge that transcends traditional design and engineering perspectives is that
new technology changes what it means to be a practitioner. The future practitioner will
not be the same in terms of knowledge, skill, expertise, and collaborative interplay as
technology changes (Mark et al., 2005). To understand and guide the process of how
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Designing for expertise 219
new technology and new designs change what people need to learn and do to be experts,
various other experts need to collaborate and transcend their individual areas of knowl-
edge. This collaboration over different kinds of experts is targeted at enhancing the
expertise in action in particular domains of human activity.
But the targets of the design have a role and a stake in terms of how new products
affect their abilities, their roles and their goals. Participatory design is more than generat-
ing collaboration across different specialists in design. The practitioner also participates
in design. Practitioners are the experts at applying information to make decisions for
actions that will affect states and behaviors in their environment – they are the experts
in the use of artifacts. Designers, on the other hand, are experts in the design decisions
that steer the development of products to support the decision making of practitioners
(Woods, 1995). Ultimately, in participatory design processes practitioners, technologists,
and innovators co-mingle each with critical but incomplete roles as designer (Roesler,
Woods and Feil, 2005).
The questions that surround the nature and acquisition of the expertise are central
to understanding design, both the mixture of experts who contribute to product designs
and the expertise in context as people use new devices to do things in the world.
The following sections are explorations into the nature of expertise: observations of
expertise in action, how we discover or elicit the knowledge organizations behind expert
performance, and how the design of new products affects the ability to acquire and dem-
onstrate expertise.
2.1. A history of the study of expertise
Practitioners base their reasoning strategies on creating and interpreting artifacts as a
form of external knowledge representation. Designers support practitioners in choosing,
structuring, and using these representations rather than relying totally on basic indi-
vidual mental capabilities without any external tools. Depending on their expertise in a
task, area, or fi eld, practitioners will engage in activities differently from what the designers
expect them to do, and may or may not arrive at anticipated outcomes used to justify
the investment in the development project. This makes expertise an important issue to
consider in design, where patterns of interaction between practitioners, artifacts, and the
world vary with different degrees and kinds of expertise.
The study of expertise has a long history that reaches back to performance evaluations
conducted by experimental psychologists in World War I-era studies of skilled machinists
(Hoffman, 1992). The methods applied in these observations date back into the 1890s and
focus basically on the timing of observable actions in the course of given tasks.
Binet’s work at the beginning of the 20th century set virtually all of the standards
that would be applied by post-1950s studies of expertise. Typically the researcher would
identify a task – for example, the selection, lifting, and alignment of a part in a machin-
ing task – and identify a typical begin and end state of the task. The researcher then
would time a worker while engaging in the process necessary to complete the task from
its beginning to end. The hypothesis was typically that the more experienced worker can
accomplish the task faster or can engage in more tasks in parallel, given time pressure.
In favoring the duration and number of tasks as central measures, these studies fall short
in capturing, discussing, and exploring the nature of the individual differences among
subjects, the situation that has preceded the test observation, and the context (such as
motivations and social structure).
These broader factors were initially considered distortions, and by transporting
the work task into a controlled laboratory, the researcher could exclude (control) the
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220 Product Experience
unwanted contextual circumstances in order to obtain objective results. It soon became
apparent that time measures of performance provided a quite limited view into the
nature of expertise. Measuring performance times in isolated sub-tasks soon evolved into
observing related activities, and in the course of this shift in focus, it emerged that many
aspects of expert decision making reside in the context, social structure, and demands
posed by different situations. In addition, it was evident that individual differences and
training played a central role in the trade of experts.
Cognitive psychologists began to study cognition and reasoning in fi eld settings.
One of the fi rst subjects studied was the expertise of chess masters (De Groot, 1946).
The reason why chess was a good subject for the study of expertise was that metrics for
the determination of chess profi ciency were already in place (Ericsson and Smith, 1991).
Furthermore, the chess board and confi guration of chess pieces serve as both physical
representation of states on the board and abstract representation in the chess player’s
conceptual model of possible – and best – next moves that would affect the constella-
tions of chess pieces on the board.
Simon and Chase’s classic study on expert chess players (1973) found that chess masters
can quickly detect meaningful patterns or confi gurations of chess piece and can remember
a very large number of confi gurations if they form meaningful patterns relative to different
situations that confront the experienced chess player. To demonstrate some of De Groot’s
observations about the capabilities of experts, Simon and Chase tested players’ ability to rec-
ognize and memorize chess positions. They presented players either with game-meaningful
confi gurations of pieces or with random confi gurations of pieces. They found that expert
players were capable of memorizing and hence planning ahead a greater number of proper
confi gurations than chess novices. When presented with random chess piece confi gurations,
however, the chess experts did not perform signifi cantly better than the novices.
According to Simon and Chase, chess masters differ from players at lower degrees
of profi ciency in that they can recall a much higher number of game-meaningful con-
gurations. This allows them to plan further ahead, given that a signifi cant constellation
forms the starting point. Compared to other chess players, the experts can assess the
consequences of many different options for moves from a current constellation in paral-
lel. Simon and Chase have shown that chess masters apply pattern recognition strategies
in their assessment of what to do next. They can do this not because of superior memory
for elements (each piece) but because they have a different knowledge organization based
on relationships that connect pieces into confi gurations and connect confi gurations into
tactical and strategic sequences of action and counter-action.
The paradigm shift from studying behavior to studying cognition in American psy-
chology was stimulated and grew along with the introduction and growth of computerized
systems into more and more realms of human activity. The development of computer appli-
cations quickly grew through attempts to reproduce human decision making or to create
computers that could make decisions on their own (called expert systems). Optimistically,
these researchers thought they would be able to replace human cognition with computerized
expert systems. While this agenda has repeatedly failed (and been repeated and failed several
more times), the goal of autonomous computers required intense study of human expertise.
Deployment of these expert computer systems quickly led to situations where the
computerized expert and the human expert were both present in the actual decision situ-
ation. The developers expected that the computer expert system would improve the deci-
sions of the human practitioner – hence they would be decision support systems. However,
providing decision support and building team work between human and machine agents
turned out to be a much more sophisticated topic and the early computer expert systems
turned out to be very poor team players. It was found that sophisticated decision support
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Designing for expertise 221
systems often had ended up being underutilized or unused by experts during their fi rst-
hand reasoning (Woods and Roth, 1990; Shanteau, 1992; Hollnagel and Woods, 2005;
Woods and Hollnagel, 2006). The reasons for this are many – computer expert systems
are inherently brittle, non-cooperative, opaque, disembodied, and designed based on erro-
neous models of expert knowledge (Hoffman, Feltovitch, and Ford, 1997).
The original attempt to understand expertise in production measures of isolated sub-
tasks had failed; the much later attempt to put expertise into a computer system and use
that system to replace people as an isolated component also failed. A third line of work
focused on studying the reasoning strategies behind expert decisions usually with a goal
of developing new training systems to speed up the acquisition of expertise as people
moved from novice to journeyman to expert.
Simon and Chase (1973) have shown that valuable insight on expert decision mak-
ing can be collected by techniques such as think-aloud verbal protocols. Hoffman (1987)
reminds us that using this and other knowledge elicitation techniques to study expert
reasoning is time consuming and leaves out important factors. Variations on think-aloud
protocols have been developed such as critical incident analysis and cognitive task analy-
sis. Today there is a wide and rich range of techniques that can be used to gain insight
about the basis of expertise in particular domains of human activity (Christoffersen
et al., 2007). By using a broad array of techniques researchers were able to see how
social constructs and organizational factors affected expertise (Wright and Bolger, 1992;
Hoffman, et al., 1997; Klein, 1997).
One of the new lines of inquiry on expertise is called naturalistic decision making
(NDM). NDM is the way people use their experience to make decisions in fi eld settings,
or, in a more comprehensive description, the study of NDM asks how experienced peo-
ple, working as individuals or groups in dynamic, uncertain, and often fast-paced envi-
ronments, identify and assess their situation, make decisions and take actions whose
consequences are meaningful to them and to the larger organization in which they oper-
ate (Zsambok and Klein, 1997; Klein, 1998). Naturalistic decision making research
underlines the role of context, social organization, and domain-specifi c knowledge in the
decision making of experts. NDM directs decision making in high-stake domains such as
re fi ghting, military command and control, mission control, aviation, and planning.
One of the key results is that expertise uses external artifacts to support the proc-
esses that contribute to expert performance – expertise is not all in the head, rather it
is distributed over a person and the artifacts they use and over the other agents they
interact with as they carry out activities, avoid failure, cope with complexity, and adapt
to disruptions and change. For an illustration on how found characteristics in the envi-
ronment become cognitive artifacts by being associated with cognitive tasks, see Edwin
Hutchins’ observations of Micronesian and Western navigation (Hutchins, 1995a).
Another key fi nding is the role of context (Woods et al., 2002). Hoffman, Feltovitch,
and Ford (1997) describe context as composed of:
• The analyst’s purposes and goals.
• The analyst’s views, assumptions, and theories.
• The analyst’s methods.
What data people regard as meaningful depends on these kind of contextual factors.
Experts are very sensitive to context shifts and how these shifts change what knowledge
and what mental conceptual models are relevant to the task at hand (Feltovich et al., 1997).
Context cannot be neglected in the study of decision making, expertise, and innovation.
This has led to the macro-cognitive view on cognitive work (Klein et al., 2003),
and with it an extended focus in the study of expertise – one that incorporates context,
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222 Product Experience
the nature of the tasks, and the social and organizational factors that identify experts.
Macro-cognition addresses the study of decision making and expertise in context and
looks at activities that are unfolding in organizations, situated in the environment of
real tasks (Hoffman, 2003; and see Woods and Roesler, in Chapter 7 of this volume).
Expertise is situated in real work settings where practitioners have experience with vari-
ations on routine performance and can access a rich set of cues that help them anticipate
what could happen next. Expert reasoning is more than a specialized algorithm which
could be captured in a computer system; expertise adapts reasoning strategies, plans and
actions in the face of changing conditions in the environment in order to achieve goals
(Woods and Hollnagel, 2006).
Overall, expertise does not reside simply in a single individual, but is situated in the
relationships between practitioner, task, environment and the organizational characteris-
tics that direct and constrain practice in the domain.
2.2. What is expertise?
A fi rst assessment of expertise considers a person’s levels of profi ciency at a task. Research
then asks the question how do people move from one level of profi ciency to another.
A basic answer is that more knowledge about the task and associated context supports
greater expertise. But this view of expertise as more extensive and better organized knowl-
edge does not take into account the role of hands on practice. Donald Schön’s (1983)
work on the intersections of knowledge and practice extends the expertise-as-knowledge
position. He focuses on the role of refl ection-in-action that occurs when knowledge is put
into practice. In his view, expertise is acquired by combining exposure to task-relevant
reasoning with task-specifi c knowledge. Profi ciency in domains such as law, medicine, and
design is the integration and mutual refi nement of knowledge, skill, and experience that
can only occur through doing.
The traditional assumption in psychology is that expertise is a phenomenon that
can be accounted for in terms of the quantity, quality, and organization of the domain-
specifi c knowledge of experts. In this view, ‘expert knowledge is based on deep refl ec-
tion, allowing experts to do well because they know more, know better, and know in a
more useable way’ (Hoffman et al., 1997). Hoffman et al. continue to question this posi-
tion by reminding us that knowledge and meaning can be seen as extra-personal, and is
located in the community rather than just inside a person. This leads us back to context,
to addressing the differences between different domains, where knowledge is constructed
and maintained in different communities. Expertise, from this angle, can be read as a
repository of strategies for applying knowledge to context by taking into account the
nature of the task and the social aspects surrounding the task. This view leads us to three
characteristics of expertise:
1. First, expertise is domain-specifi c.
2. Second, experts adapt to changes in their environment.
3. Third, experts rarely act as isolated individuals.
Summarizing various studies of expertise in the agricultural, medical, weather fore-
casting, and law sectors, Shanteau (1992) identifi es fi ve psychological strategies that help
experts in making decisions:
1. Expert judgment shows a willingness to make continuous adjustments in initial
2. Experts get help from others in order to make better decisions when coping with
uncertainty. They can identify the experts in sub-domains.
[AU2] [AU3]
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Designing for expertise 223
3. Experts make use of both formal and informal external decision aids.
4. Even though experts may make small errors in judgment, they avoid making
large mistakes. ‘The focus is not on being exactly right, but on avoiding making
bad decisions.’ To do this, experts fi rst conduct a broad estimate of a problem
situation and then conduct a more careful analysis of components in the situation
that were identifi ed as more critical in the fi rst pass.
5. Experts decompose complex situations into manageable chunks and can then
re-construct the larger situation from its individual aspects.
These fi ve strategies indicate that experts have access to a quite refi ned conceptual
model of their domain of expertise to identify functional relationships, dependencies, and
likely outcomes. This model of the domain includes scenarios of what not to do (Minsky,
1997). The expert’s conceptual model also includes an understanding of relationships of
their domain with sub-domains that are part of the challenges ahead. Experts know who
the experts are in these sub-domains, and when to consult them.
Expertise forms a decision making capacity in the neighborhood of other forces.
Hoffman, Feltovitch and Ford illustrate that expertise entails context as a set of other
factors that surround and direct expertise with their TEMPEST model (Figure 8.1).
The tempest model has evolved from previous models as new research results have
accumulated. The original base is James Jenkins’ (1979) tetrahedron model that captured
results from learning research. Honeck and Temple (1992) adapted a version to incorpo-
rate new results on the cognitive underpinnings of expertise. Building on these models, the
TEMPEST model maps expertise as the tetrahedral set of relationships between the expert’s
strategies, goals, background experience, and materials used as decision support.
FIGURE 8.1 TEMPEST model of expertise in context (modifi ed from Hoffman, Feltovitch and Ford, 1997).
background experience
knowledge and skills
of the
familiar tasks
used in familiar tasks
data, tools used in
the familiar tasks
the ‘kite’
the expert
cognition, perception
the ‘tail’
stabilizing forces
selection criteria,
training methods,
professional standards
the ‘line’
controlling forces
lawfulness in the world,
practical constraints,
accountability mechanisms and
performance expectations
the ‘wind’
driving forces
societal needs and expectations,
personal motives
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224 Product Experience
As a diagram, the two-dimensional tetrahedron resembles a kite, and represents the
expert. In taking the kite analogy further, Hoffman et al. illustrate the context of exper-
tise in showing that it takes more to fl y a kite than the kite alone.
• The wind carries the kite – in the TEMPEST model, the wind resembles the
driving forces on expertise, rooted in societal needs and expectations and personal
• The kite is steered by the line – controlling forces on expertise that are
institutionalized by lawfulness in the world, practical constraints, accountability
mechanisms and performance expectations.
• The kite is stabilized in the wind by its tail – the stabilizing forces of expertise are
selection criteria, training methods, and professional standards for experts.
• Tail and line make sure the kite stays stable in the wind and can be controlled.
• Together with the wind as the driving force, they form the context of expertise,
and if we look at the labels of the contextual forces, we encounter many of the
sociological parameters that identify expertise.
Note how it takes the interactions between all factors in the diagram to constitute,
identify, and steer expertise, and how the four subsystems (kite, wind, line, and tail) can
partially compensate for defi ciencies in other aspects. If the kite, for example becomes
unstable due to a tail that is too short, more reactive steering with the line is required to
keep the kite in the air.
The TEMPEST model illustrates expertise as a macro-cognitive system of interre-
lated factors of organizational structure, environmental conditions, decision making,
knowledge, and planning ahead. Characteristic of this macro-cognitive representation of
expertise is that experts balance multiple factors in parallel, and that all factors represent
diversity and contrasting forces that mutually reinforce another. Growing expertise to act
effectively requires orchestrating the interplay over this set of factors and driving forces.
2.3. Who are the experts?
Expertise is both a social construct and the context-specifi c application and adaptation
of knowledge. Studies on the quality of expert decisions have shown that experts do not
generally make better decisions than do non-experts (Shanteau, 1992). This contrasts
with the general perception that experts can be identifi ed by their outstanding perform-
ance, and hints at a social dimension behind the identifi cation of experts. In virtually
every organization, certain people are known as the best ones to consult for specifi c
types of problems.
Though experts do not always make the best decisions in a given situation, they have
been shown to avoid making decisions that they expect to have particularly negative
consequences (Shanteau, 1992). Expert decision making seems to be driven by appro-
priateness, not necessarily by correctness. Non-experts in similar situations are prone to
making wrong decisions, because often, they cannot assess the repercussions of alterna-
tive decisions. Experts will likely avoid making wrong decisions, but will opt for a solu-
tion with the least trade-off if they do not see the possibility for an optimal solution.
Since experts have a conceptual model of their domain, they know what not to do (how
to avoid errors of over-control of complex dynamic processes).
Societal institutions play a role in the accreditation of experts. While some experts
have been identifi ed by peers for their remarkable performance in a number of situations,
other specialists are accredited through licensing systems such as university degrees and
academic titles. Certifi cates document that an expert has access to an agreed-upon body
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Designing for expertise 225
of knowledge and that he or she has demonstrated performance in a number of practice
situations, as well as passed examinations of expected performance.
Knowing who the experts are in a domain can be a challenge. While domain practi-
tioners seem to have no problem in identifying the experts among themselves, outsiders
have diffi culties identifying who the experts are in a particular domain (Sternberg, 1997).
Utilizing insights from the study of expertise, let us refer to a list of nine charac-
teristics that identify an expert (Shanteau, 1992). Experts have access to a vast body of
up-to-date content knowledge; as a basis for judgment, this deep level understanding of
their domain is characterized by:
• Highly developed perceptual/attention abilities: Experts can extract information in
their domain that non-experts miss (Fiore and Hoffman, 2007).
Case-based reasoning in context: Experts have a sense of what is relevant when
they are making decisions. Where novices often get sidetracked by irrelevant data,
experts base their decisions on less information, but ensure that their judgment rests on
relevant information.
• The ability to simplify complex problems: Experts have developed strategies to
decompose complicated situations in their domain into smaller chunks. While novices
attempt decomposition, they often fall into making oversimplifi cations. This is not true
for experts, because they know that their domain is composed of several sub-domains.
The ability to decompose complicated context is guided by a conceptual model of the
domain that the expert has constructed (Feltovich et al., 1997).
• The ability to communicate expertise: Experts have no problem communicat-
ing information in the areas where they are experts. They assume that their assessments
should be preferred over those of others. This outspoken expertise goes hand-in-hand
with the social construction of expertise: that confi dence in claiming oneself as an expert
can accredit a person as expert. Let us keep in mind that this statement is put to the test
until our expert unites all nine characteristics in this list.
The ability to handle diversity more successfully than non-experts: Experts con-
tinue to make effective decisions even when things are not going well. This characteristic
is again supported by the expert’s comprehensive conceptual model of his or her domain.
This model does not only include best case scenarios, but also includes forks in the road
where things can go wrong, together with scenarios of how not-so-optimal sequences
will play out and how to recover from them.
• The ability to identify and adapt to exceptions: Experts are not likely to walk
down ‘garden paths.’ They can identify when an expected situation deviates from the
Knowing how and when to adapt their decision strategies to changing task condi-
tions: Experts detect when changes in the context of the task demand adapted reason-
ing strategies. This is possible because experts have access to many alternative response
strategies and understand that different situations cannot all be approached with the
same strategy.
• Strong self-confi dence: Experts trust their decisions.
A strong sense of responsibility: Experts can assess how situations will likely play
out. They understand their role as expert decision-makers and are aware of the conse-
quences of a wrong decision. In any case, experts know that they must make decisions
because they can control the situation at hand.
Naturally, these characteristics of expertise make experts diffi cult people to work
with. They are confi dent in their expertise, and they can assess the expertise of others in
their domain. This makes expertise an interesting topic from the training and learning
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226 Product Experience
perspective, where domain experts help novices in eliciting domain knowledge to eventu-
ally become experts themselves.
2.4. How expertise is acquired
Dreyfus and Dreyfus (1986, 1997) have identifi ed fi ve stages in the acquisition of exper-
tise, where a novice starts at a beginner level, moves continually through levels of com-
petence and profi ciency, and fi nally arrives at the expert level. The fi ve stages illustrate
increasing levels of profi ciency in performance as well as the role of context-free rules,
the realization of patterns in context, and the hierarchical organization of task-relevant
clues for decision making or intuitive response.
Stage 1: Novice – The beginner engages in an instruction sequence where he or she
learns context-free features of the domain that can be recognized without experience in
the domain. Beginners in chess, for example, learn a numeric value for each type of piece
regardless of its playing value. They also learn a rule that is associated with the piece, as
well as the positioning constraints on the piece. Beginners are slow performers during
this stage because they need to remember and recall the rules that guide their actions.
Stage 2: Advanced beginner – At this stage, the beginner is pointed to specifi c fea-
tures that provide situational aspects of the activity. He or she begins to see patterns in
the context of the task that provide additional information in support of the rules.
Stage 3: Competence – At the competent level, the student continues to identify
more task-relevant features in the context of the activity. This eventually results in a
large number of patterns that he or she recognizes, culminating in the sheer overload of
potentially task-relevant data. To cope with complexity, the student begins to structure
patterns and strategies by installing hierarchical reasoning sequences. The competent
performer can address a broad spectrum of situations. Performance is slow and depends
on the student’s seeking new rules and reasoning strategies in order to devise a plan or
take a perspective.
Stage 4: Profi cient – In the profi cient stage, intuitive engagement in the activities
continually replaces reasoned responses. Instead of being rule-based, the reasoning strat-
egies of the performer are driven by adapting decision support structures to the current
context of a situation. The performer simply knows what to do instead of deciding in the
course of a calculative response.
Stage 5: Expertise – While the profi cient performer sees what needs to be done
and decides to do it, the expert performer not only knows what needs to be done, but
also knows how to achieve the desired state. This allows the immediate response to a
clearly identifi ed situation. This immediate and appropriate response is characteristic of
Dreyfus and Dreyfus’ fi ve stages in acquiring expertise focus on the student’s journey.
If the student were required to rely solely on his or her individual experience, the extent
of lessons learned would depend on the variety of failures that confronted the student,
so that limitations would have to be overcome by practice. Ericsson and Charness (1997)
point out the importance of deliberate practice in the acquisition of expertise. In delibera-
tive practice, students work with a teacher or coach whose job is to identify individual
weaknesses and to design training programs that repeatedly confront students with the
weak aspects of their performance. The continuous confrontation engages the student in
developing response strategies for overcoming individual weakness.
In their study of the training and development of expertise in outstanding musical
performers and athletes, Ericsson and Charness identify four developmental stages, from
the novice stage to that of eminent expert. Particularly interesting in their observations
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Designing for expertise 227
is that they look at an extended temporal frame of expert development, encompassing
the three to fi ve decades spanning acquisition of knowledge, skill, and endurance in the
development from the beginner stage towards the highest level of expertise.
The extremely long training period required to achieve the mastery of musical per-
formance or world class competition level in a sport is necessary to provide for the
mutual adaptation between the performer’s or athlete’s physical capabilities, and the
advancement of expertise and endurance during practice.
In the course of their classic study on chess, Simon and Chase (1973) have identifi ed
that it typically takes ten years of intensive deliberative practice to arrive at an expert
level of performance in chess. Studies in other domains (Ericsson, Krampe and Tesch-
Römer, 1993) have confi rmed this estimate. Ericsson and Charness’ four phases in the
course of becoming an eminent expert pass through the following profi ciency levels:
1. In Phase one, musical performers and athletes, at a young age, engage in playful
activities in the domain. This phase ends with the identifi cation of potential in the per-
former, indicating that he or she can move the acquired skills into the next level.
2. Phase two is an extended preparation. It ends with the commitment of engaging
in practice full-time. Usually, this phase entails the consultation of specialized trainers
or coaches, and often, even requires moving into another geographic region where the
required training with an advanced teacher or coach is available.
3. In Phase three, the performer makes a commitment to full-time engagement in the
training and practice of the skill. This phase ends either with the performer being able to
make a living as a professional, or with the complete termination of the full-time activ-
ity (Bloom, 1985). At this stage, the performer is practicing or training with an expert
teacher or coach. During the fi rst three stages, students integrate the knowledge and skill
that master teachers and coaches can convey (Ericsson and Charness, 1997).
4. To achieve the highest level of expertise – the fourth phase – performers must
attain a level of performance where they make an original contribution to the domain
of their expertise. In this phase, they reach a level of profi ciency that goes beyond that
of their teachers. At the eminent level, where the highest level of expertise is innovation,
empirical studies of these original contributions have been sparse. This is attributed to
the fact that the moment at which the contribution will occur is hard to predict. Ideas,
conditions, and decisions that have led to major breakthroughs in thinking and prac-
tice can be traced by retrospective process tracing methods (Gruber, 1981; Wallace and
Gruber, 1989).
Ericsson and Charness’ (1997) observation that innovation is an indicator of an eminent
state of expertise draws parallels to the diffi culties that grip the concept of innovation in
design circles. As practitioners designers tend to know what innovation is, yet a compre-
hensive description of innovation in design is still largely missing. Like important discov-
eries by scientists and creative leaps by artists and performers, innovation in design can
often only be studied in retrospect, since it is diffi cult to experimentally set up the condi-
tions that would allow an innovative breakthrough to be observed as it unfolds. Such
staging techniques would be more than desirable to designers. Perhaps design methods
could be considered as a direction in the quest to demystify innovation; a methodic design
approach enables designers to observe, provide, and steer environments and design condi-
tions so that they provide fertile grounds for innovations to ‘happen’.
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228 Product Experience
The design process is the structuring of resource pools that enables designers to
envision promising design responses for future iterations of a product or system, based
on observations of the performance of current design solutions for that product or sys-
tem in the fi eld. These observations eventually result in a domain understanding for the
designer. In its sequence of formation, this development of insight shows many parallels
to the sequence of acquiring expert understanding. Although the sequence of learning
from observations plays out in a much shorter timeframe and is conducted from an emic
perspective of the designer (from a view inside the culture of design), the insight won by
designers makes it possible for them to create novel interventions relative to the target
domain of the expert practitioner.
As a consequence of the shorter time frame spent in the expert domain, the insight
won by the designers is much less comprehensive than the deep level domain understand-
ing acquired by an expert over a span of many years. One possible explanation of these
limitations is that the designers are limited to observations and often cannot practice
the observed operations of use, since they are not suffi ciently prepared. Knowing this
restriction, the challenge for designers is in knowing how observations in the fi eld can
be linked to the design of concepts that respond to identifi ed problems or needs in the
domain of practice.
In the design process, the acquisition of expertise can be viewed as an iterative cycle
of feedback, concept generation, and selection. In the cycle, observations are intertwined
with modeling of a conceptual representation of the fi eld of practice under observation;
conceptual representations with actions to create objects that can fi elded. Figures 8.2A
and B illustrate a cyclical framework that can be used to chart these design process, the
de:cycle model (Roesler and Woods, 2005).
Design activity, to be able to respond to the challenges provided by the call for novelty
balanced with pressures on realizablity, coordinates expertise from three perspectives –
observe, explore, create. These provide linkage points between three broad roles that
draw on pools of experience – pools of research patterns, technological possibilities,
and experience in the fi eld. Each of these roles provides their knowledge, processes, and
artifacts with respect to the future under design: practitioner – how they adapt to com-
plexity, innovator – how they envision what would be useful, and technologist – how
they bring the anticipated change into the world of practice. The three roles intersect,
and design activity within them is conducted in parallel – which provides for numerous
interactions across the center of the de:cycle.
In a typical design project, design activity begins with observations in the fi eld of
practice where practitioners identify limitations of previously fi elded objects. In the
de:cycle graphic, this point of departure is located at the 4 o’clock position: design intent
is represented in clockwise movements; design effects are projected in counterclockwise
Moving counterclockwise for exposition purposes, in the observation arc (northeast
section), design researchers plan studies, collect data records from protocols, cognitive
task analyses, and other process tracing techniques. Functional syntheses based on the
data and observations leads to abstract models that capture patterns, provide explana-
tions, and lead to propose alternative directions. For example, researchers may design
observations to reveal how practitioners have adapted products to work for them in
ways different from the designer’s model (design intent is represented in clockwise move-
ments). Different explanations and characterizations of the basis for these adaptations
then become seeds for exploring possible new design directions.
In the exploration arc (northwest section), there is movement from the models, pat-
terns and data of research to design seeds that capture promising concepts hypothesized
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Designing for expertise 229
to lead to useful change in the target fi eld of practice. We refer to this arc in the de:cycle
as the ‘Northwest Passage’ as the processes of innovation and ideation are not supported
systematically, but rather have tended to be supported or marked by design artifacts per-
sonal to specifi c designers or teams (at least as compared to the more formal artifacts
produced by design in the realization process, e.g. those that mark software develop-
ment). In the Northwest Passage, ideation mediates between research insights about the
situation under design (rotating clockwise) and technological capacity for change (rotat-
ing counter-clockwise) as related to judgments about desired improvements form the
point of view of practitioners (cutting across the center of the cycle from left to right).
FIGURE 8.2 (A) The de:cycle – The three roles in design and their respective interests and expertise.
Counterclockwise moves represent design synthesis, as objects are created. Clockwise moves represent the analysis
of design that identifi es the origins, basis, and expectations associated with design decisions. (B) Handover marks and
design process products make design activity tangible and allow designers to plan for deadlines and coordinate with
other stakeholders.
developing scenarios
developing concepts
building prototypes
detailing for production deployment into the field
listening to stories
observations of adaptations
stories of use
data records
abstracted patterns
abstract model
leverage points
representation techniques
design seeds
hypotheses of what is useful
model of the ideal design
final design model
design documentation
reproduction model
plans and documentation
toolingtest realese
in field revision 1
in field revision 2
established field product
unsatisfactory product
suggestions for improvement
adjusted final release
pools of technological
innovator: pools of research patterns
pools of experience
in the field
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230 Product Experience
The techniques of those profi cient in innovation generate insights to generate promising
concepts that can be turned into prototype demonstrations that support commitment of
resources for realizing these concepts in fi eldable forms.
In the implementation arc (southern section), technologists choose from a pool of
available technologies those that are appropriate for the realization of selected design
concepts. The appropriateness of applied technologies is evaluated against factors of
functionality, reliability, feasibility, safety, and economic feasibility – and how necessary
modifi cations will refl ect on the original design program.
Figure 8.2B shows how handovers across the roles serve as demarcations and how
products produced in the process of design, such as scenarios, sketches, mock-ups and
prototypes, make these processes tangible helping designers refl ect on their progress and
share ideas with other stakeholders in the design process.
To processes of design, focusing on the need to create innovations, can be seen as
a type of expertise that demonstrates the same regularities as have been summarized
in the preceding sections of the chapter. As designers become acquainted with a situa-
tion under design they incrementally attain a deeper level understanding of the design
domain. A deepening understanding facilitates the quest for innovation as in the defi ni-
tion of the eminent level of expertise. Figure 8.3 shows the cycle of expertise acquisition
and domain change, overlaid on the de:cycle framework.
In design, one moves from novice, relative to the target fi eld of practice, to greater
expertise, relative to how that domain could function. The design process meets the cri-
teria for eminent expertise with the generation of innovative concepts, though these will
turn out to have surprising effects when fi elded which will release a new round of adap-
tation through use.
In the observation arc, designers who had entered the design situation as novices
have moved through stages of advanced beginner, competence, and profi ciency. Starting
at the stage of profi ciency, they have begun building a conceptual representation
around the acquired knowledge of the domain as research fi ndings. While the evolving
extracting What’s interesting
(reasoning backward)
identifying what’s promising
(reasoning forward)
detailling responses implementing change
directing observations
novice advanced beginner
eminent expert
FIGURE 8.3 The cyclical model of the acquisition of expertise is overlaid on the de:cycle, a cyclical
model of the design process. The overlay illustrates how innovation as a result of the build up to eminent stage of
expertise produces change that leads to effects that require the growth of new expertise.
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Designing for expertise 231
conceptual model provides the profi cient designer with explanations for what has been
observed, it allows the capacity to make predictions by simulating the outcomes of
proposed changes that would be part of new products.
Because the conceptual model is a representation of the relationship, dependen-
cies, and causal sequences identifi ed during observations in the fi eld, it allows experts
to reason forward by manipulating variables in the model. Moving counter-clockwise
is a process of projecting ahead toward future states. These predictions form hypoth-
eses about how investments in additional design work will produce change in the target
eld of human activity. These hypotheses are tentative and open to revision as evidence
comes in about the likely impact of the proposed changes. One diffi culty is that getting
this evidence usually requires movement into activities that make design ideas tangible
in the form of sketches, mock-ups, and refi ned prototypes. These types of design repre-
sentations allow designers to share their observations with the domain practitioners and
design clients.
Taking this as a starting point, let us return to the nature of expertise in a more gen-
eral frame of reference. Practice is characterized by an experimental component, where
the expert follows through with promising actions based on decisions that were sup-
ported by simulations performed in the framework of relationships of the conceptual
model. Activities are subsequently evaluated in the context of the real situation before
being adapted to unanticipated conditions and modifi ed in a later iteration. Any expert
is designing response strategies based on a hard-won conceptual understanding of the
situation at hand.
The eminent expert knows that in order to identify the boundaries of their under-
standing, they have to manipulate the situation at hand. This alteration of existing con-
ditions is done responsibly, as he or she can identify manipulations that would lead to
unmanageable outcomes. The eminent expert also knows the scope and possible results
of unmanageable states, and will avoid losing control of the situation.
The stage of eminent expertise is characterized by innovation that had originated in
the course of many previous observations of such experiments. These previous experi-
ments do not need to be repeated for the current decision task. The past outcome of the
original experiment or practice is suffi cient for making the current decision.
The eminent expert recycles past insight, accessing and applying patterns from a
knowledge base of past storylines. The expert has learned to calibrate projections, based
on appropriate interventions that conform to the constraint space identifi ed as boundary
conditions. Yet the eminent expert carries out an intervention to provide more insight
into the situation at hand and does not rely completely on his or her knowledge of the
relevant constraint space. This is the process of envisioning innovation.
Eminent expertise is characterized by the continual refi nement of a conceptual model
of the expert’s domain. This occurs by directing observations with the intent to actively
manipulate conditions in the domain in order to arrive at innovative new outcomes
from given and known situations. The eminent expert does this repeatedly, as he or she
purposefully steers activity away from routine. The eminent expert knows about the lim-
its of routines and knows how to apply slight changes to the repeated activity in order
to scope new boundaries. As he or she initiates new conditions in the known fi eld, the
expert will eventually fi nd him/herself in new terrain, hence being less than an eminent
expert in the newly created domain area. In a cyclical confi guration of gathering under-
standing and generating novelty by applying the new understanding, the knowledge base
of the domain expert grows with each iteration of change.
As with designers who fi rst need to learn about the domain under design before
being able to initiate change, the eminent expert explores the boundaries of the current
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232 Product Experience
conceptual models by purposefully manipulating conditions in his or her domain.
Designing – the act of manipulating representations – is necessary in order to arrive at
refi ned representations. Eminent expertise thus is not the mastery of analytic deductions
in the found domain, but instead incorporates steering the domain by changing it. The
original contribution of the eminent expert is a necessity in this process of gathering
advanced understanding to correlate actual results with anticipated outcomes. This proc-
ess is the engagement of the expert in refl ective practice (Schön, 1983) where strategies
for activities, as well as the activities themselves, are subject to adaptations in the face of
a changing environment.
Domain experts have gone through many iterations of refl ection upon action, initi-
ated by changes in the conditions within the domain environment and driven by results
and problems that arise from these changes. These iterations are necessary to reinforce
mutual dependencies between conceptual assessment and practical application on the
basis of a series of interventions into ongoing activities. The eminent expert’s conceptual
model of the domain has reached a level that it can represent the patterns of change that
steer the adaptation of response strategies to the changing context of the situation.
Expert decision making is driven by context. From the perspective of the designer
who faces the challenges of designing artifacts as decision support, this has many
First, the nature of expertise is rooted in the social structure of the domain and
is driven by the changing nature of the domain context as captured in the TEMPEST
model of expertise as a kite in the wind (Figure 8.1). Expertise is generally instantiated
as an interpersonal collaboration where designers can identify experts in certain stages of
the activities, while expert roles may change during different stages of tasks. This same
pattern is relevant to the design process, where the lead expert roles shift between prac-
titioners, innovators and technologists (Roesler et al., 2005). In order to conduct the
design process, all three pools of expertise are necessary in parallel. No person can play
all roles; designers cannot completely put themselves into the seat of practitioners, prac-
titioners cannot take over design work, and technologists cannot replace designers – nor
can they take on the role of practitioner. Problems arise in the design process when one
pool of expertise attempts to stand in for others on the basis of inferences from a dis-
tance. Key to an understanding of expertise in design is coordinating the convergence of
these different types of expertise as a particular product development cycle matures and
There are many examples where the handover of role-specifi c expertise is central.
Consider, for example, the changing meanings of artifacts in the course of cognitive work
with the model of a door key. While a technologist is primarily concerned with designing
a secure key that can reliably lock and unlock a door, the innovator might question the
concept of a key to lock a door at all. The question might not stop at this point. Why
a door at all, what is it for? What are other possibilities for the concept of a key? From
the perspective of a practitioner, the existence of the key becomes meaningful only as
specifi c episodes arise during the execution of tasks. On the way to the site of a task, the
practitioner encounters a locked door. Unlocking the door is not the central task for
the practitioner – getting to the other side of the door where the task is located is the
central task.
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Designing for expertise 233
Let’s put this apparently simple issue in a context: during an emergency at a power
plant a pump that should provide a critical function to maintain safe conditions fails to
start. The person on the scene is inexperienced and is unable to start the pump; the expe-
rienced person is in a different location in the plant (the control room) and attempts to
get to the failed pump to perform the restart. However, during an emergency for security
reasons physical access from one part of the plant to another is restricted. The key expert
is blocked from reaching the critical location; if the pump is not restarted an extreme
and untried safety procedure must be started. Can the expert fi nd a way through the
blocked doors in time to restart the critical pump in time? (He did in the actual nuclear
power plant emergency in 1985 that is the basis for this example.)
This unique event captures a general tradeoff between security and collaboration –
blocking access when it is undesirable or blocking some groups access versus enabling
collaboration and connection to enhance performance – that is a central dilemma in cur-
rent debates about computer security in a world of web-based interactions at a distance.
One pool of expertise is about different ways to block access; another pool of exper-
tise is about ways to enable more collaboration at a distance. Without coordinating the
two, cycles of mal-adaptation develop where for security reasons access is hobbled or
blocked and people who can take advantage of the connection devise workarounds to
In reverse, we can deduct levels of expertise from artifact characteristics in the work
environments of experts. The expert work of aircraft pilots is mapped into the complex
structure of the aviation cockpit (which in turn is a representation of the complexities
underlying the task of fl ying a plane – a representation of the joint system of different
people and devices that underlies fl ying; see Hutchins 1995b). There is a link between
expertise and the complexity of technology applications. Alexander (1964) has argued
that for emerging specializations, the emergence of experts is the consequence of techno-
logical advances and part of the formation of new domains.
Understanding the context-dependent nature of expertise is a prerequisite for designing
artifacts that support decision making – that allow people to become familiar with patterns
of use and that support identifying constraints necessary to detect and resolve irregularities
in the course of action. From the cognitive systems design perspective, the level of expertise
of domain practitioners directs how much of the guidance for use must be built into the
artifacts of a system, since the training of experts may serve as a substitute in the absence
of self-explanatory clues in expert environments. As an example, compare a pilot with the
everyday driver of an automobile. For the former, fl ight training typically takes a few hun-
dred hours, with frequent deliberate refresher sessions in the fl ight simulator. As a conse-
quence, the pilot has an extensive conceptual representation of the plane as a system of
artifacts, and of the plane’s boundaries of performance in the face of environmental con-
dition changes and other events. This expertise acts as a source of resilience to overcome
design defi ciencies such as mode confusions so that they rarely turn in to plane crashes (but
see the mode confusion issue in, for example, Woods et al., 1994).
Now consider the car example. Driver training is much less elaborate and consists
of a rough understanding of traffi c rules, performance of the vehicle, and control of the
vehicle. The domain of driving a car in roadway traffi c is less complex than that of fl ying
an airplane in air traffi c. Compared to the domain of fl ying a plane, the driving domain
consists of factors such as slower speed and simpler degree of technology in the ground-
bound vehicle, greater visibility of traffi c and obstacles, and a greater proximity margin
of other traffi c participants.
In contrast, the airplane pilot must think further ahead, due to the higher speed of
the plane, and must cope with the complex technology of a vehicle that cannot stop
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234 Product Experience
without losing aerodynamic lift. In addition, the pilot is a participant in a larger collabo-
rative organization consisting of cockpit crew, airport crew, and air traffi c controller – all
in different roles – where limited fl ight, take-off, and landing pathways must be shared
with other planes within very tight schedules. Add to this complex organizational con-
glomerate automated on-board systems, replanning due to delays, invisible fl ight routes,
and eventual technical problems, and it’s easy to assess why pilots are experts.
We have learned how diffi cult the assessment of typical practitioner expertise is.
Expert users can draw resources for interpretation of gaps from their individual knowl-
edge, while novice users rely on knowledge in the world that is provided by the design of
the task environment (Norman, 1986).
While providing an extended range of performance, advanced technological applica-
tions introduce new forms of complexity, and with them new possible failures. During
the past thirty years, advanced computerized systems in high-stakes domains have dem-
onstrated the new realities of increased sophistication in the form of new possibilities.
Unfortunately, they also have demonstrated the new realities of increased complexi-
ties that have contributed to new types of incidents and accidents in aviation, process
control, monitoring, medicine, mission control, and military applications (Woods and
Hollnagel, 2006).
Due to the public consequences of failures, extensive studies about the nature of
expertise in the monitoring and design of technology have generated invaluable insight
into the relationship between domain knowledge, training, and design. In all instances,
highly trained experts are paired with advanced computerized technology in joint sys-
tems (Hollnagel and Woods, 2006). In facing the challenges of designing for these expert
domains, it is essential that designers realize that their objective is not to design for users,
but to support experts by design.
Learning how to reinforce innovation is a form of cognitive work as refl ection-
in-action. Design requires understanding, exploration, and experimentation; during
ideation phases in design projects, this cognitive work leads to novel artifacts and new
approaches of doing things, always coupled with the initiation of change. New techno-
logical artifacts will change the nature of practice (Winograd and Flores, 1986). Novel
artifacts will require practitioners to engage in new types of operations that need to be
learned. In the long run, innovation will form new types of expertise, while experts will
make contributions that lead to new inventions (Woods and Dekker, 2000).
Designers are confronted with the challenge of understanding the expertise in the
domain under design; this does not require that they become experts in the domain
themselves. Studies of expertise in various fi elds of practice, conducted during the past
50 years – among them studies on the expertise of designers by Cross (2003) – illustrate
the nature of expertise as a characteristic feature in cognitive work that is tightly coupled
with the nature of the fi eld of practice. The most recent view on expertise is that it exists
in the context of a specifi c domain and encompasses the social structure of the fi eld, indi-
vidual differences, training, and personal experience.
Another important fi nding is that expertise is subject to continuous change. As tech-
nologies advance, they transform the fi eld of practice and confront experts with the
necessity of keeping their knowledge updated.
In the face of these characteristics of expertise, it seems quite illusionary that design-
ers could become experts in a fi eld they encounter during a design assignment. However,
designers may fi nd it helpful to become familiar with the patterns of knowledge elici-
tation and work practice that instantiate expertise at work (see the earlier section on
‘What is expertise’). By designing support for expert reasoning strategies in collabora-
tion with practitioners and technology developers, designers make their contribution to
CH008.indd 234CH008.indd 234 9/27/07 6:26:50 PM9/27/07 6:26:50 PM
Designing for expertise 235
expert work. Key to the design of systems that support practitioners in being experts is
a design process model that allows the convergence of the perspectives of practitioners,
innovators and technologists.
After excursions through the nature, study of, and various views on expertise; through
the implications of expertise research on design; and through the strategies for a design
process that addresses the introduction of novelty into the domains of experts, we can
now summarize expertise in a few sentences to illustrate how aspects of expert practice,
decision making, and innovation-building affect designers.
Our view on expertise enables designers to design with the objective of supporting
the work of experts and acquiring expertise. Expertise as a focus for design presents an
interesting though somewhat moving target for design activity, since expertise is driven
by the context and social structure of the domain. One of the consequences of this is
that expertise continuously re-invents itself in a sequence of adaptations to a changing
environment. At the same time, events that drive these changes are, in part, outcomes of
expert practice.
We summarize expertise as follows:
• Experts have observed, learned and practiced in their domain for a long time.
Their expertise is domain-specifi c, driven by the context of the domain, and instantiated
in the social structure of the domain.
• Expertise is both knowledge and skill in the adaptation of understanding to
observations in the context of a situation.
Expert practitioners capture and model their domain understanding and approach
strategies in representations that they refi ne while eliciting the knowledge required to
practice as experts in their domain.
• Expertise changes; the agreed-upon standards of outstanding performance are sub-
ject to continuous improvement. Improvements may include refi ned reasoning strategies,
better decision support representations, or more advanced adaptations to the changing
nature of practice. In addition, social standards on how to assess expertise may change.
• Expertise is a form of contextual understanding that guides the formation of
strategies in making sense of observations in a given context.
• Expertise is limited by the perspective of the expert. Complex decision making
tasks, such as designing, require reasoning strategies that utilize methods of coordination
between the different types of expertise of design stakeholders. It is not only advisable to
consult other perspectives, it is necessary (Klein, 1997).
At the eminent of level of expertise, an expert generates an original contribution
to the standards of the domain. As innovators, experts shape the future of succeeding
experts in their fi eld. In the course of learning to understand their domain, the eminent
experts change their domain, and the changed domain requires and forms a new genera-
tion of experts, often trained by the eminent experts, and a product of the new conditions.
This eminent level shows parallels to the key challenge in design work: the generation and
creation of novelty, and the responsibilities to steer the changed conditions.
Expertise, in conclusion, evolves as a consequence of adaptations in a changing environ-
ment. In parallel, expertise forms the basis from where practitioners and designers envision
and implement changes in the form of novelty into the domain. Novel technologies, in turn,
will initiate new forms of expertise. Innovation and expertise are mutually interrelated.
CH008.indd 235CH008.indd 235 9/27/07 6:26:50 PM9/27/07 6:26:50 PM
236 Product Experience
Assessing the expertise of future practitioners is both a requirement and an outcome
of design support. Expertise does not reside in artifacts or people, but is the result of
cognitive work by practitioners who build a correspondence across artifacts with their
goals, their environment, the perspectives of others in their fi eld of practice, and the
stakeholders who depend on the outcomes produced.
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***Author query **
[AU1]AU: Woods et al., 2002 not in refs. Pl provide details
[AU2]AU: Hoffman 2003 not in refs. Pl provide details
[AU3]AU: Chapter Xref OK?
[AU4]AU: Jenkins 1979 not in refs. Pl provide details
[AU5]AU: Ericsson et al. 1993 not in refs. Pl provide details
[AU6]AU: Published yet? Please give page nos.
[AU7]AU: Published yet? Please give page nos.
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Full-text available
Nothing has been more prolific over the past century than human/machine interaction. Automobiles, telephones, computers, manufacturing machines, robots, office equipment, machines large and small; all affect the very essence of our daily lives. However, this interaction has not always been efficient or easy and has at times turned fairly hazardous. Cognitive Systems Engineering (CSE) seeks to improve this situation by the careful study of human/machine interaction as the meaningful behavior of a unified system. Written by pioneers in the development of CSE, Joint Cognitive Systems: Foundations of Cognitive Systems Engineering offers a principled approach to studying human work with complex technology. The authors use a top-down, functional approach and emphasize a proactive (coping) perspective on work that overcomes the limitations of the structural human information processing view. They describe a conceptual framework for analysis with concrete theories and methods for joint system modeling that can be applied across the spectrum of single human/machine systems, social/technical systems, and whole organizations. The book explores both current and potential applications of CSE illustrated by examples. Understanding the complexities and functions of the human/machine interaction is critical to designing safe, highly functional, and efficient technological systems. This is a critical reference for students, designers, and engineers in a wide variety of disciplines.
Full-text available
Our fascination with new technologies is based on the assumption that more powerful automation will overcome human limitations and make our systems 'faster, better, cheaper,' resulting in simple, easy tasks for people. But how does new technology and more powerful automation change our work? Research in Cognitive Systems Engineering (CSE) looks at the intersection of people, technology, and work. What it has found is not stories of simplification through more automation, but stories of complexity and adaptation. When work changed through new technology, practitioners had to cope with new complexities and tighter constraints. They adapted their strategies and the artifacts to work around difficulties and accomplish their goals as responsible agents. The surprise was that new powers had transformed work, creating new roles, new decisions, and new vulnerabilities. Ironically, more autonomous machines have created the requirement for more sophisticated forms of coordination across people, and across people and machines, to adapt to new demands and pressures. This book synthesizes these emergent Patterns though stories about coordination and mis-coordination, resilience and brittleness, affordance and clumsiness in a variety of settings, from a hospital intensive care unit, to a nuclear power control room, to a space shuttle control center. The stories reveal how new demands make work difficult, how people at work adapt but get trapped by complexity, and how people at a distance from work oversimplify their perceptions of the complexities, squeezing practitioners. The authors explore how CSE observes at the intersection of people, technology, and work, how CSE abstracts patterns behind the surface details and wide variations, and how CSE discovers promising new directions to help people cope with complexities. The stories of CSE show that one key to well-adapted work is the ability to be prepared to be surprised. Are you ready?.
Experts, who were the sole active dispensers of certain kinds of knowledge in the days before AI, have now often assumed a rather passive role. They relay their knowledge to various novices, knowledge engineers, experimental psychologists or cognitivists - or other experts! - involved in the development and understanding of expert systems. This book achieves a perfect marriage between experimentalists and theoreticians who deal with expertise. It tries to establish the benefits to society of an advanced technology for representing and disseminating the knowledge and skills of the best corporate managers, the most seasoned pilots, and the most renowned medical diagnosticians. This book interests psychologists as well as all those out in the trenches developing expert systems, and everyone pondering the nature of expertise and the question of how it can be studied scientifically. Its scope, the pivotal concepts which it elucidates and brilliantly summarizes and appraises in the final chapter, as well as the references it includes, make this book a landmark in the field.
This article is a commentary, reflecting on the special section ‘Applications for naturalistic decision-making’ (Gore, Flin, Stanton, Wong, 2015, J. Occup. Organ. Psychol., doi:10.1111/joop.12121).