Description
Evaluate how well your home on-demand streaming system is designed (how the information is organized and how well the information is communicated/presented). Define the following 3 elements for your home on-demand streaming system, and evaluate how well the design satisfies the following (define all of these for your on-demand streaming system): Identify the high-level user needs for an on-demand streaming system: what do customers want and need from an on-demand streaming system during these stressful COVID times? (For example, Speed, Convenience, a wide selection of titles, etc.)List all of the relevant high level user needs that you can think of. Define the capabilities and characteristics of these 3 user groups: Children (6-12) Young adults (18 – 30) Older adults (65 years and olderFor each of these 3 user groups, identify what their capabilities and characteristics are that would impact their ability to use the on-demand streaming system.For example, poor eyesight (for older adults)For example, small hand size (for teenagers) *Note: these are the capabilities and characteristics that the likely users of the on-demand streaming system will possess: not the user capabilities required by virtue of how the on-demand streaming system is designed!Environments of use: a description of the environment in which the on-demand streaming system is used, and what that environment implies as far as the design of the on-demand streaming system is concerned.I’m looking for an in-depth analysis here! So, if you tell me it will be used in the dark, then you would comment on whether or not the design supports using it in the dark (e.g., does it illuminate in the dark?) Reading reading resources attached and USE THE TEMPLATE PROVIDED when you wirte.enough to trigger the behavioral system, causing us to turn, run, and
flee. Here is where the cognition sets off the fear and the action.
Most products do not cause fear, running, or fleeing, but badly
designed devices can induce frustration and anger, a feeling of
helplessness and despair, and possibly even hate. Well-designed
devices can induce pride and enjoyment, a feeling of being in control and pleasure—possibly even love and attachment. Amusement parks are experts at balancing the conflicting responses of
the emotional stages, providing rides and fun houses that trigger
fear responses from the visceral and behavioral levels, while all
the time providing reassurance at the reflective level that the park
would never subject anyone to real danger.
All three levels of processing work together to determine a person’s cognitive and emotional state. High-level reflective cognition
can trigger lower-level emotions. Lower-level emotions can trigger
higher-level reflective cognition.
The Seven Stages of Action
and the Three Levels of Processing
The stages of action can readily be associated with the three different levels of processing, as shown in Figure 2.4. At the lowest level
are the visceral levels of calmness or anxiety when approaching a
task or evaluating the state of the world. Then, in the middle level,
are the behavioral ones driven by expectations on the execution
side—for example, hope and fear—and emotions driven by the
confirmation of those expectations on the evaluation side—for example, relief or despair. At the highest level are the reflective emotions, ones that assess the results in terms of the presumed causal
agents and the consequences, both immediate and long-term. Here
is where satisfaction and pride occur, or perhaps blame and anger.
One important emotional state is the one that accompanies complete immersion into an activity, a state that the social scientist
Mihaly Csikszentmihalyi has labeled “flow.” Csikszentmihalyi
has long studied how people interact with their work and play,
and how their lives reflect this intermix of activities. When in the
flow state, people lose track of time and the outside environment.
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They are at one with the task
they are performing. The task,
moreover, is at just the proper
level of difficulty: difficult
enough to provide a challenge
and require continued attention, but not so difficult that it
invokes frustration and anxiety.
Csikszentmihalyi’s work
shows how the behavioral
level creates a powerful set of
emotional responses. Here, the
subconscious expectations esF IGU R E 2 . 4 . Levels of Processing and the
Stages of the Action Cycle. Visceral response is tablished by the execution side
at the lowest level: the control of simple muscles of the action cycle set up emoand sensing the state of the world and body. The
behavioral level is about expectations, so it is sen- tional states dependent upon
sitive to the expectations of the action sequence those expectations. When the
and then the interpretations of the feedback. The
results of our actions are evalreflective level is a part of the goal- and plan-setting activity as well as affected by the comparison uated against expectations, the
of expectations with what has actually happened.
resulting emotions affect our
feelings as we continue through
the many cycles of action. An easy task, far below our skill level, makes
it so easy to meet expectations that there is no challenge. Very little or
no processing effort is required, which leads to apathy or boredom. A
difficult task, far above our skill, leads to so many failed expectations
that it causes frustration, anxiety, and helplessness. The flow state occurs when the challenge of the activity just slightly exceeds our skill
level, so full attention is continually required. Flow requires that the
activity be neither too easy nor too difficult relative to our level of skill.
The constant tension coupled with continual progress and success can
be an engaging, immersive experience sometimes lasting for hours.
People as Storytellers
Now that we have explored the way that actions get done and the
three different levels of processing that integrate cognition and
emotion, we are ready to look at some of the implications.
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People are innately disposed to look for causes of events, to form
explanations and stories. That is one reason storytelling is such
a persuasive medium. Stories resonate with our experiences and
provide examples of new instances. From our experiences and the
stories of others we tend to form generalizations about the way
people behave and things work. We attribute causes to events, and
as long as these cause-and-effect pairings make sense, we accept
them and use them for understanding future events. Yet these
causal attributions are often erroneous. Sometimes they implicate
the wrong causes, and for some things that happen, there is no
single cause; rather, a complex chain of events that all contribute
to the result: if any one of the events would not have occurred, the
result would be different. But even when there is no single causal
act, that doesn’t stop people from assigning one.
Conceptual models are a form of story, resulting from our predisposition to find explanations. These models are essential in helping
us understand our experiences, predict the outcome of our actions,
and handle unexpected occurrences. We base our models on whatever knowledge we have, real or imaginary, naive or sophisticated.
Conceptual models are often constructed from fragmentary evidence, with only a poor understanding of what is happening, and
with a kind of naive psychology that postulates causes, mechanisms, and relationships even where there are none. Some faulty
models lead to the frustrations of everyday life, as in the case of my
unsettable refrigerator, where my conceptual model of its operation (see again Figure 1.10A) did not correspond to reality (Figure
1.10B). Far more serious are faulty models of such complex systems as an industrial plant or passenger airplane. Misunderstanding there can lead to devastating accidents.
Consider the thermostat that controls room heating and cooling
systems. How does it work? The average thermostat offers almost
no evidence of its operation except in a highly roundabout manner. All we know is that if the room is too cold, we set a higher
temperature into the thermostat. Eventually we feel warmer. Note
that the same thing applies to the temperature control for almost
any device whose temperature is to be regulated. Want to bake a
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cake? Set the oven thermostat and the oven goes to the desired
temperature.
If you are in a cold room, in a hurry to get warm, will the room
heat more quickly if you turn the thermostat to its maximum setting? Or if you want the oven to reach its working temperature
faster, should you turn the temperature dial all the way to maximum, then turn it down once the desired temperature is reached?
Or to cool a room most quickly, should you set the air conditioner
thermostat to its lowest temperature setting?
If you think that the room or oven will cool or heat faster if the
thermostat is turned all the way to the maximum setting, you are
wrong—you hold an erroneous folk theory of the heating and cooling system. One commonly held folk theory of the working of a
thermostat is that it is like a valve: the thermostat controls how
much heat (or cold) comes out of the device. Hence, to heat or cool
something most quickly, set the thermostat so that the device is on
maximum. The theory is reasonable, and there exist devices that
operate like this, but neither the heating or cooling equipment for a
home nor the heating element of a traditional oven is one of them.
In most homes, the thermostat is just an on-off switch. Moreover,
most heating and cooling devices are either fully on or fully off:
all or nothing, with no in-between states. As a result, the thermostat turns the heater, oven, or air conditioner completely on, at full
power, until the temperature setting on the thermostat is reached.
Then it turns the unit completely off. Setting the thermostat at
one extreme cannot affect how long it takes to reach the desired
temperature. Worse, because this bypasses the automatic shutoff
when the desired temperature is reached, setting it at the extremes
invariably means that the temperature overshoots the target. If
people were uncomfortably cold or hot before, they will become
uncomfortable in the other direction, wasting considerable energy
in the process.
But how are you to know? What information helps you understand how the thermostat works? The design problem with the
refrigerator is that there are no aids to understanding, no way of
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forming the correct conceptual model. In fact, the information
provided misleads people into forming the wrong, quite inappropriate model.
The real point of these examples is not that some people have erroneous beliefs; it is that everyone forms stories (conceptual models) to explain what they have observed. In the absence of external
information, people can let their imagination run free as long as
the conceptual models they develop account for the facts as they
perceive them. As a result, people use their thermostats inappropriately, causing themselves unnecessary effort, and often resulting
in large temperature swings, thus wasting energy, which is both a
needless expense and bad for the environment. (Later in this chapter, page 69, I provide an example of a thermostat that does provide a useful conceptual model.)
Blaming the Wrong Things
People try to find causes for events. They tend to assign a causal relation whenever two things occur in succession. If some unexpected
event happens in my home just after I have taken some action, I am
apt to conclude that it was caused by that action, even if there really
was no relationship between the two. Similarly, if I do something expecting a result and nothing happens, I am apt to interpret this lack
of informative feedback as an indication that I didn’t do the action
correctly: the most likely thing to do, therefore, is to repeat the action,
only with more force. Push a door and it fails to open? Push again,
harder. With electronic devices, if the feedback is delayed sufficiently,
people often are led to conclude that the press wasn’t recorded, so
they do the same action again, sometimes repeatedly, unaware that
all of their presses were recorded. This can lead to unintended results.
Repeated presses might intensify the response much more than was
intended. Alternatively, a second request might cancel the previous
one, so that an odd number of pushes produces the desired result,
whereas an even number leads to no result.
The tendency to repeat an action when the first attempt fails
can be disastrous. This has led to numerous deaths when people
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tried to escape a burning building by attempting to push open exit
doors that opened inward, doors that should have been pulled. As
a result, in many countries, the law requires doors in public places
to open outward, and moreover to be operated by so-called panic
bars, so that they automatically open when people, in a panic to
escape a fire, push their bodies against them. This is a great application of appropriate affordances: see the door in Figure 2.5.
Modern systems try hard to provide feedback within 0.1 second
of any operation, to reassure the user that the request was received.
This is especially important if the operation will take considerable
time. The presence of a filling hourglass or rotating clock hands is
a reassuring sign that work is in progress. When the delay can be
predicted, some systems provide time estimates as well as progress
bars to indicate how far along the task has gone. More systems
should adopt these sensible displays to provide timely and meaningful feedback of results.
Some studies show it is wise to underpredict—that is, to say an
operation will take longer than it actually will. When the system
computes the amount of time, it can compute the range of possible
FIGURE 2 . 5. Panic Bars on Doors. People fleeing a fire would die if they encountered exit doors that opened inward, because they would keep trying to push
them outward, and when that failed, they would push harder. The proper design,
now required by law in many places, is to change the design of doors so that they
open when pushed. Here is one example: an excellent design strategy for dealing
with real behavior by the use of the proper affordances coupled with a graceful
signifier, the black bar, which indicates where to push. (Photograph by author at the
Ford Design Center, Northwestern University.)
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times. In that case it ought to display the range, or if only a single
value is desirable, show the slowest, longest value. That way, the
expectations are liable to be exceeded, leading to a happy result.
When it is difficult to determine the cause of a difficulty, where
do people put the blame? Often people will use their own conceptual models of the world to determine the perceived causal relationship between the thing being blamed and the result. The word
perceived is critical: the causal relationship does not have to exist;
the person simply has to think it is there. Sometimes the result is
to attribute cause to things that had nothing to do with the action.
Suppose I try to use an everyday thing, but I can’t. Who is at
fault: me or the thing? We are apt to blame ourselves, especially if
others are able to use it. Suppose the fault really lies in the device,
so that lots of people have the same problems. Because everyone
perceives the fault to be his or her own, nobody wants to admit
to having trouble. This creates a conspiracy of silence, where the
feelings of guilt and helplessness among people are kept hidden.
Interestingly enough, the common tendency to blame ourselves
for failures with everyday objects goes against the normal attributions we make about ourselves and others. Everyone sometimes
acts in a way that seems strange, bizarre, or simply wrong and
inappropriate. When we do this, we tend to attribute our behavior
to the environment. When we see others do it, we tend to attribute
it to their personalities.
Here is a made-up example. Consider Tom, the office terror. Today, Tom got to work late, yelled at his colleagues because the office coffee machine was empty, then ran to his office and slammed
the door shut. “Ah,” his colleagues and staff say to one another,
“there he goes again.”
Now consider Tom’s point of view. “I really had a hard day,” Tom
explains. “I woke up late because my alarm clock failed to go off: I
didn’t even have time for my morning coffee. Then I couldn’t find
a parking spot because I was late. And there wasn’t any coffee in
the office machine; it was all out. None of this was my fault—I had
a run of really bad events. Yes, I was a bit curt, but who wouldn’t
be under the same circumstances?”
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Tom’s colleagues don’t have access to his inner thoughts or to his
morning’s activities. All they see is that Tom yelled at them simply
because the office coffee machine was empty. This reminds them of
another similar event. “He does that all the time,” they conclude,
“always blowing up over the most minor things.” Who is correct?
Tom or his colleagues? The events can be seen from two different points of view with two different interpretations: common responses to the trials of life or the result of an explosive, irascible
personality.
It seems natural for people to blame their own misfortunes on
the environment. It seems equally natural to blame other people’s
misfortunes on their personalities. Just the opposite attribution, by
the way, is made when things go well. When things go right, people credit their own abilities and intelligence. The onlookers do
the reverse. When they see things go well for someone else, they
sometimes credit the environment, or luck.
In all such cases, whether a person is inappropriately accepting
blame for the inability to work simple objects or attributing behavior to environment or personality, a faulty conceptual model is
at work.
LEARNED HELPLESSNESS
The phenomenon called learned helplessness might help explain the
self-blame. It refers to the situation in which people experience repeated failure at a task. As a result, they decide that the task cannot
be done, at least not by them: they are helpless. They stop trying.
If this feeling covers a group of tasks, the result can be severe difficulties coping with life. In the extreme case, such learned helplessness leads to depression and to a belief that the individuals cannot
cope with everyday life at all. Sometimes all it takes to get such a
feeling of helplessness are a few experiences that accidentally turn
out bad. The phenomenon has been most frequently studied as a
precursor to the clinical problem of depression, but I have seen it
happen after a few bad experiences with everyday objects.
Do common technology and mathematics phobias result from
a kind of learned helplessness? Could a few instances of failure
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in what appear to be straightforward situations generalize to every technological object, every mathematics problem? Perhaps. In
fact, the design of everyday things (and the design of mathematics
courses) seems almost guaranteed to cause this. We could call this
phenomenon taught helplessness.
When people have trouble using technology, especially when
they perceive (usually incorrectly) that nobody else is having the
same problems, they tend to blame themselves. Worse, the more
they have trouble, the more helpless they may feel, believing that
they must be technically or mechanically inept. This is just the opposite of the more normal situation where people blame their own
difficulties on the environment. This false blame is especially ironic
because the culprit here is usually the poor design of the technology, so blaming the environment (the technology) would be completely appropriate.
Consider the normal mathematics curriculum, which continues
relentlessly on its way, each new lesson assuming full knowledge
and understanding of all that has passed before. Even though each
point may be simple, once you fall behind it is hard to catch up.
The result: mathematics phobia—not because the material is difficult, but because it is taught so that difficulty in one stage hinders
further progress. The problem is that once failure starts, it is soon
generalized by self-blame to all of mathematics. Similar processes
are at work with technology. The vicious cycle starts: if you fail
at something, you think it is your fault. Therefore you think you
can’t do that task. As a result, next time you have to do the task,
you believe you can’t, so you don’t even try. The result is that you
can’t, just as you thought.
You’re trapped in a self-fulfilling prophecy.
POSITIVE PSYCHOLOGY
Just as we learn to give up after repeated failure, we can learn optimistic, positive responses to life. For years, psychologists focused
upon the gloomy story of how people failed, on the limits of human abilities, and on psychopathologies—depression, mania, paranoia, and so on. But the twenty-first century sees a new approach:
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to focus upon a positive psychology, a culture of positive thinking,
of feeling good about oneself. In fact, the normal emotional state
of most people is positive. When something doesn’t work, it can
be considered an interesting challenge, or perhaps just a positive
learning experience.
We need to remove the word failure from our vocabulary, replacing it instead with learning experience. To fail is to learn: we learn
more from our failures than from our successes. With success, sure,
we are pleased, but we often have no idea why we succeeded. With
failure, it is often possible to figure out why, to ensure that it will
never happen again.
Scientists know this. Scientists do experiments to learn how the
world works. Sometimes their experiments work as expected, but
often they don’t. Are these failures? No, they are learning experiences. Many of the most important scientific discoveries have
come from these so-called failures.
Failure can be such a powerful learning tool that many designers
take pride in their failures that happen while a product is still in
development. One design firm, IDEO, has it as a creed: “Fail often,
fail fast,” they say, for they know that each failure teaches them a
lot about what to do right. Designers need to fail, as do researchers. I have long held the belief—and encouraged it in my students
and employees—that failures are an essential part of exploration
and creativity. If designers and researchers do not sometimes fail, it
is a sign that they are not trying hard enough—they are not thinking the great creative thoughts that will provide breakthroughs in
how we do things. It is possible to avoid failure, to always be safe.
But that is also the route to a dull, uninteresting life.
The designs of our products and services must also follow this
philosophy. So, to the designers who are reading this, let me give
some advice:
• Do not blame people when they fail to use your products properly.
• Take people’s difficulties as signifiers of where the product can be
improved.
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• Eliminate all error messages from electronic or computer systems.
Instead, provide help and guidance.
• Make it possible to correct problems directly from help and guidance
messages. Allow people to continue with their task: Don’t impede
progress—help make it smooth and continuous. Never make people
start over.
• Assume that what people have done is partially correct, so if it is
inappropriate, provide the guidance that allows them to correct the
problem and be on their way.
• Think positively, for yourself and for the people you interact with.
Falsely Blaming Yourself
I have studied people making errors—sometimes serious ones—
with mechanical devices, light switches and fuses, computer operating systems and word processors, even airplanes and nuclear
power plants. Invariably people feel guilty and either try to hide
the error or blame themselves for “stupidity” or “clumsiness.” I
often have difficulty getting permission to watch: nobody likes to
be observed performing badly. I point out that the design is faulty
and that others make the same errors, yet if the task appears simple or trivial, people still blame themselves. It is almost as if they
take perverse pride in thinking of themselves as mechanically
incompetent.
I once was asked by a large computer company to evaluate a
brand-new product. I spent a day learning to use it and trying
it out on various problems. In using the keyboard to enter data, it
was necessary to differentiate between the Return key and the Enter key. If the wrong key was pressed, the last few minutes’ work
was irrevocably lost.
I pointed out this problem to the designer, explaining that I,
myself, had made the error frequently and that my analyses indicated that this was very likely to be a frequent error among users.
The designer’s first response was: “Why did you make that error?
Didn’t you read the manual?” He proceeded to explain the different functions of the two keys.
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“Yes, yes,” I explained, “I understand the two keys, I simply confuse
them. They have similar functions, are located in similar locations on
the keyboard, and as a skilled typist, I often hit Return automatically,
without thought. Certainly others have had similar problems.”
“Nope,” said the designer. He claimed that I was the only person who had ever complained, and the company’s employees had
been using the system for many months. I was skeptical, so we
went together to some of the employees and asked them whether
they had ever hit the Return key when they should have hit Enter.
And did they ever lose their work as a result?
“Oh, yes,” they said, “we do that a lot.”
Well, how come nobody ever said anything about it? After all,
they were encouraged to report all problems with the system. The
reason was simple: when the system stopped working or did something strange, they dutifully reported it as a problem. But when
they made the Return versus Enter error, they blamed themselves.
After all, they had been told what to do. They had simply erred.
The idea that a person is at fault when something goes wrong is
deeply entrenched in society. That’s why we blame others and even
ourselves. Unfortunately, the idea that a person is at fault is imbedded in the legal system. When major accidents occur, official courts
of inquiry are set up to assess the blame. More and more often the
blame is attributed to “human error.” The person involved can
be fined, punished, or fired. Maybe training procedures are revised.
The law rests comfortably. But in my experience, human error usually
is a result of poor design: it should be called system error. Humans
err continually; it is an intrinsic part of our nature. System design
should take this into account. Pinning the blame on the person may
be a comfortable way to proceed, but why was the system ever designed so that a single act by a single person could cause calamity?
Worse, blaming the person without fixing the root, underlying cause
does not fix the problem: the same error is likely to be repeated by
someone else. I return to the topic of human error in Chapter 5.
Of course, people do make errors. Complex devices will always
require some instruction, and someone using them without instruction should expect to make errors and to be confused. But
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designers should take special pains to make errors as cost-free as
possible. Here is my credo about errors:
Eliminate the term human error. Instead, talk about communication and interaction: what we call an error is usually bad communication or interaction. When people collaborate with one another, the word error is never used to characterize another person’s
utterance. That’s because each person is trying to understand
and respond to the other, and when something is not understood
or seems inappropriate, it is questioned, clarified, and the collaboration continues. Why can’t the interaction between a person
and a machine be thought of as collaboration?
Machines are not people. They can’t communicate and understand the same way we do. This means that their designers have
a special obligation to ensure that the behavior of machines is understandable to the people who interact with them. True collaboration requires each party to make some effort to accommodate
and understand the other. When we collaborate with machines, it
is people who must do all the accommodation. Why shouldn’t the
machine be more friendly? The machine should accept normal human behavior, but just as people often subconsciously assess the
accuracy of things being said, machines should judge the quality of
information given it, in this case to help its operators avoid grievous errors because of simple slips (discussed in Chapter 5). Today,
we insist that people perform abnormally, to adapt themselves to
the peculiar demands of machines, which includes always giving
precise, accurate information. Humans are particularly bad at this,
yet when they fail to meet the arbitrary, inhuman requirements of
machines, we call it human error. No, it is design error.
Designers should strive to minimize the chance of inappropriate actions in the first place by using affordances, signifiers,
good mapping, and constraints to guide the actions. If a person
performs an inappropriate action, the design should maximize
the chance that this can be discovered and then rectified. This
requires good, intelligible feedback coupled with a simple, clear
conceptual model. When people understand what has happened,
what state the system is in, and what the most appropriate set of
actions is, they can perform their activities more effectively.
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People are not machines. Machines don’t have to deal with
continual interruptions. People are subjected to continual interruptions. As a result, we are often bouncing back and forth between tasks, having to recover our place, what we were doing,
and what we were thinking when we return to a previous task.
No wonder we sometimes forget our place when we return to the
original task, either skipping or repeating a step, or imprecisely
retaining the information we were about to enter.
Our strengths are in our flexibility and creativity, in coming up
with solutions to novel problems. We are creative and imaginative,
not mechanical and precise. Machines require precision and accuracy; people don’t. And we are particularly bad at providing precise
and accurate inputs. So why are we always required to do so? Why
do we put the requirements of machines above those of people?
When people interact with machines, things will not always
go smoothly. This is to be expected. So designers should anticipate this. It is easy to design devices that work well when everything goes as planned. The hard and necessary part of design is to
make things work well even when things do not go as planned.
HOW TECHNOLOGY CAN ACCOMMODATE HUMAN BEHAVIOR
In the past, cost prevented many manufacturers from providing
useful feedback that would assist people in forming accurate
conceptual models. The cost of color displays large and flexible
enough to provide the required information was prohibitive for
small, inexpensive devices. But as the cost of sensors and displays
has dropped, it is now possible to do a lot more.
Thanks to display screens, telephones are much easier to use than
ever before, so my extensive criticisms of phones found in the earlier
edition of this book have been removed. I look forward to great improvements in all our devices now that the importance of these design principles are becoming recognized and the enhanced quality
and lower costs of displays make it possible to implement the ideas.
P R OV I D I N G A C O N C E P T UA L M O D E L F O R A H O M E T H E R M O S TAT
My thermostat, for example (designed by Nest Labs), has a colorful
display that is normally off, turning on only when it senses that I
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F IGU RE 2 .6. A Thermostat with an Explicit Conceptual Model. This thermostat, manufactured by Nest Labs,
helps people form a good conceptual model of its operation. Photo A shows the thermostat. The background, blue,
indicates that it is now cooling the home. The current temperature is 75°F (24°C) and the target temperature is 72°F
(22°C), which it expects to reach in 20 minutes. Photo B
shows its use of a smart phone to deliver a summary of its
settings and the home’s energy use. Both A and B combine
to help the home dweller develop conceptual models of
the thermostat and the home’s energy consumption. (Pho-
A.
tographs courtesy of Nest Labs, Inc.)
B.
am nearby. Then it provides me with the current temperature of
the room, the temperature to which it is set, and whether it is heating or cooling the room (the background color changes from black
when it is neither heating nor cooling, to orange while heating, or
to blue while cooling). It learns my daily patterns, so it changes
temperature automatically, lowering it at bedtime, raising it again
in the morning, and going into “away” mode when it detects that
nobody is in the house. All the time, it explains what it is doing.
Thus, when it has to change the room temperature substantially
(either because someone has entered a manual change or because
it has decided that it is now time to switch), it gives a prediction:
“Now 75°, will be 72° in 20 minutes.” In addition, Nest can be connected wirelessly to smart devices that allow for remote operation
of the thermostat and also for larger screens to provide a detailed
analysis of its performance, aiding the home occupant’s development of a conceptual model both of Nest and also of the home’s energy consumption. Is Nest perfect? No, but it marks improvement
in the collaborative interaction of people and everyday things.
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E N T E R I N G DAT E S , T I M E S , A N D T E L E P H O N E N U M B E R S
Many machines are programmed to be very fussy about the form
of input they require, where the fussiness is not a requirement of
the machine but due to the lack of consideration for people in the
design of the software. In other words: inappropriate programming. Consider these examples.
Many of us spend hours filling out forms on computers—forms
that require names, dates, addresses, telephone numbers, monetary sums, and other information in a fixed, rigid format. Worse,
often we are not even told the correct format until we get it wrong.
Why not figure out the variety of ways a person might fill out a
form and accommodate all of them? Some companies have done
excellent jobs at this, so let us celebrate their actions.
Consider Microsoft’s calendar program. Here, it is possible to
specify dates any way you like: “November 23, 2015,” “23 Nov.
15,” or “11.23.15.” It even accepts phrases such as “a week from
Thursday,” “tomorrow,” “a week from tomorrow,” or “yesterday.”
Same with time. You can enter the time any way you want: “3:45
PM,” “15.35,” “an hour,” “two and one-half hours.” Same with
telephone numbers: Want to start with a + sign (to indicate the code
for international dialing)? No problem. Like to separate the number fields with spaces, dashes, parentheses, slashes, periods? No
problem. As long as the program can decipher the date, time, or
telephone number into a legal format, it is accepted. I hope the
team that worked on this got bonuses and promotions.
Although I single out Microsoft for being the pioneer in accepting a wide variety of formats, it is now becoming standard practice. By the time you read this, I would hope that every program
would permit any intelligible format for names, dates, phone numbers, street addresses, and so on, transforming whatever is entered
into whatever form the internal programming needs. But I predict
that even in the twenty-second century, there will still be forms
that require precise accurate (but arbitrary) formats for no reason
except the laziness of the programming team. Perhaps in the years
that pass between this book’s publication and when you are read-
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ing this, great improvements will have been made. If we are all
lucky, this section will be badly out of date. I hope so.
The Seven Stages of Action:
Seven Fundamental Design Principles
The seven-stage model of the action cycle can be a valuable design tool, for it provides a basic checklist of questions to ask. In
general, each stage of action requires its own special design strategies and, in turn, provides its own opportunity for disaster. Figure
2.7 summarizes the questions:
1. What do I want to accomplish?
2. What are the alternative action sequences?
3. What action can I do now?
4. How do I do it?
5. What happened?
6. What does it mean?
7. Is this okay? Have I accomplished my goal?
Anyone using a product should always be able to determine the
answers to all seven questions. This puts the burden on the designer
F I G U R E 2 . 7. T he Seven
Stages of Action as Design
Aids. Each of the seven stages
indicates a place where the
person using the system has a
question. The seven questions
pose seven design themes.
How should the design convey the information required
to answer the user’s question?
Through appropriate constraint and mappings, signifiers and conceptual models,
feedback and visibility. The
information that helps answer
questions of execution (doing)
is feedforward. The information
that aids in understanding
what has happened is feedback.
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to ensure that at each stage, the product provides the information
required to answer the question.
The information that helps answer questions of execution (doing) is feedforward. The information that aids in understanding
what has happened is feedback. Everyone knows what feedback is.
It helps you know what happened. But how do you know what
you can do? That’s the role of feedforward, a term borrowed from
control theory.
Feedforward is accomplished through appropriate use of signifiers, constraints, and mappings. The conceptual model plays an
important role. Feedback is accomplished through explicit information about the impact of the action. Once again, the conceptual
model plays an important role.
Both feedback and feedforward need to be presented in a form that
is readily interpreted by the people using the system. The presentation has to match how people view the goal they are trying to achieve
and their expectations. Information must match human needs.
The insights from the seven stages of action lead us to seven fundamental principles of design:
1. Discoverability. It is possible to determine what actions are possible
and the current state of the device.
2. Feedback. There is full and continuous information about the results
of actions and the current state of the product or service. After an
action has been executed, it is easy to determine the new state.
3. Conceptual model. The design projects all the information needed
to create a good conceptual model of the system, leading to understanding and a feeling of control. The conceptual model enhances
both discoverability and evaluation of results.
4. Affordances. The proper affordances exist to make the desired actions possible.
5. Signifiers. Effective use of signifiers ensures discoverability and that
the feedback is well communicated and intelligible.
6. Mappings. The relationship between controls and their actions follows the principles of good mapping, enhanced as much as possible
through spatial layout and temporal contiguity.
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7. Constraints. Providing physical, logical, semantic, and cultural constraints guides actions and eases interpretation.
The next time you can’t immediately figure out the shower control in a hotel room or have trouble using an unfamiliar television
set or kitchen appliance, remember that the problem is in the design. Ask yourself where the problem lies. At which of the seven
stages of action does it fail? Which design principles are deficient?
But it is easy to find fault: the key is to be able to do things
better. Ask yourself how the difficulty came about. Realize that
many different groups of people might have been involved, each
of which might have had intelligent, sensible reasons for their actions. For example, a troublesome bathroom shower was designed
by people who were unable to know how it would be installed,
then the shower controls might have been selected by a building
contractor to fit the home plans provided by yet another person.
Finally, a plumber, who may not have had contact with any of the
other people, did the installation. Where did the problems arise? It
could have been at any one (or several) of these stages. The result
may appear to be poor design, but it may actually arise from poor
communication.
One of my self-imposed rules is, “Don’t criticize unless you can
do better.” Try to understand how the faulty design might have
occurred: try to determine how it could have been done otherwise.
Thinking about the causes and possible fixes to bad design should
make you better appreciate good design. So, the next time you
come across a well-designed object, one that you can use smoothly
and effortlessly on the first try, stop and examine it. Consider how
well it masters the seven stages of action and the principles of design. Recognize that most of our interactions with products are actually interactions with a complex system: good design requires
consideration of the entire system to ensure that the requirements,
intentions, and desires at each stage are faithfully understood and
respected at all the other stages.
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Human Factors Design Examples
Human Factors Design Examples
Human Factors Design Examples
Confusing instructions; lengthy process
Human Factors Design Examples
A little better
Good Design or Bad Design?
User need:
– Communicate/ Remind
– Temporary
Tasks
– Write
– Erase
– Store
Good Design or Bad Design?
Lifted Video
Lifted discussion – think of all the HF related issues
• Audio-off
• No visual identifier – coding
• No conceptual model
• No mappings, no labels
• No feedback
• could see the outcome but couldn’t use the feedback from system
• No feedback from the supervisor
• Under stress you are not yourself (survival instinct – flight or fight)
• No manual
• Everything afforded pushing
Recap from last week
Bell curve – Normal distribution curves (remote control example)
• First, notice that the graph is symmetrical – so that 50% of people are of average
height or taller, and 50% are of average height or smaller.
• The graph tails off to either end, because fewer people are extremely tall or very
short. To the left of the average, there is a point known as the 5th percentile, because
5% of the people (or 1 person in 20) is shorter than this particular height.
• The same distance to the right is a point known as the 95th percentile, where only 1
person in 20 is taller than this height.
5th%ile
50th %ile
95th %ile
5′ 4″
5′ 9″
6′ 1″
Female over 20 4’11”
5′ 3″
5′ 8”
Male over 20
Recap from last week
Using Bell Curve Data
http://www.ergonomics4schools.com/lzone/anthropometry.htm
Anthropometry
Anthropometry (Greek anthropos (“man”) and
metron (“measure”) therefore
“measurement of man” refers to the
measurement of the human individual.
This is the branch of ergonomics that deals with
body shape and size. People come in all shapes
and sizes so you need to take these physical
characteristics into account whenever you
design anything that someone will use, from
something as simple as a pencil to something as
complex as a car.
Today, anthropometry plays an important role in
industrial design, clothing design, ergonomics and
architecture where statistical data about the
distribution of body dimensions in the population
are used to optimize products.
Anthropometry
• Percentile Range (generally uses 5%-90% range)
• User Population Definition (e.g. early astronauts vs. civilian astronauts)
• Misuse of the 50th Percentile (does not accommodate everyone)
• Summation of Segment Dimensions
• For 95%, not all body segments are 95%
o “Caution must be taken when combining body segment dimensions. The 95th
percentile arm length, for instance, is not the addition of the 95th percentile
shoulder-to-elbow length plus the 95th percentile elbow-to-hand length. The actual
95th percentile arm length will be somewhat less. The 95th percentile individual is
not composed of 95th percentile segments. The same is true for any percentile
individual.” – NASA
• Consider measurements such as: Height, strength, weight, space, reach,
envelope, conformability
Application of Anthropometric Data in Design
Equipment, whether it be a workstation or clothing, must fit the user population. The
user population will vary in size, and the equipment design must account for this range
of sizes.
There are three ways in which a design will fit the user:
a. Single Size For All – A single size may accommodate all members of the population.
A workstation which has a switch located within the reach limit of the smallest person,
for instance, will allow everyone to reach the switch.
b. Adjustment – The design can incorporate an adjustment capability. The most
common example of this is the automobile seat.
c. Several Sizes – Several sizes of equipment may be required to accommodate the full
population size-range. This is usually necessary for equipment or personal gear that
must closely conform to the body such as clothing and space suits
All three situations require the designer to use anthropometric data.
Application of Anthropometric Data in Design
Herman Miller “Aeron Chair”
– Three sizes, very adjustable
Application of Anthropometric Data in Design
Herman Miller “Sayl Chair”
– One size, very adjustable
Anthropometric Tables and Charts
How to use anthropometry
Nasa anthropometric data
Task Analysis
Task analysis examines the steps that must traversed in order to attain a goal.
– What tasks are associated with getting money out of an ATM, or with
making a sandwich?
TEMPLATE FOR STREAMING MOVIE (PART 1) ASSIGNMENT
Name
Finding/Streaming a Movie, Part 1 Assignment
1. High Level User Needs (add a brief description of what this means)
● Need #1
○
Thoroughly describe what it is and why it’s a need.
● Need #2
○
Thoroughly describe what it is and why it’s a need.
● Need #3
●
○
Etc…
Thoroughly describe what it is and why it’s a need.
2. User Capabilities/Characteristics (add a brief description of what this means)
● Children (10 years old and under)
●
●
○ Characteristic (add a brief description of why this characteristic is particular to this
user group)
○ Characteristic (add a brief description of why this characteristic is particular to this
user group)
○ Characteristic (add a brief description of why this characteristic is particular to this
user group)
○ Etc…
Young Adults (18-30 yrs.)
○ Characteristic (add a brief description of why this characteristic is particular to this
user group)
○ Characteristic (add a brief description of why this characteristic is particular to this
user group)
○ Characteristic (add a brief description of why this characteristic is particular to this
user group)
○ Etc…
Older Adults (65 yrs. and older)
○ Characteristic (add a brief description of why this characteristic is particular to this
user group)
○ Characteristic (add a brief description of why this characteristic is particular to this
user group)
○ Characteristic (add a brief description of why this characteristic is particular to this
user group)
○ Etc…
3. Environment of Use
○ Describe the environment of use. Be detailed… is the environment a living room,
bedroom, etc.? How is the user positioned (ex: seated in a chair, lying on the floor)?
What is the lighting like? When is the system being used? What type of device is being
used?
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