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Human Computer Interaction

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compatible with standards. The spirit of the law, not the letter of the law, should be
your guide.
Lecture
40
Lecture 40. Observing User
Learning Goals
As the aim of this lecture is to introduce you the study of Human Computer
Interaction, so that after studying this you will be able to:
·  Discuss the benefits and challenges of different types of observation.
·  Discuss how to collect, analyze and present data from observational
evaluation.
Observation involves watching and listening to users. Observing users interacting
with software, even casual observing, can tell you an enormous amount about what
they do, the context in which they do it, how well technology supports them, and what
other support is needed. In this lecture we describe how to observe and do
ethnography and discuss their role in evaluation.
User can be observed in controlled laboratory-like conditions, as in usability testing,
or in the natural environments in which the products are used--i.e., the field. How the
observation is done depends on why it is being done and the approach adopted. There
is a variety of structured, less structured, and descriptive observation techniques for
evaluators to choose from. Which they select and how their findings are interpreted
will depend upon the evaluation goals, the specific questions being addressed, and
practical constraints.
What and when to observe
40.1
Observing is useful at any time during product development. Early in design,
observation helps designers understand users' needs. Other types of observation
are done later to examine whether the developing prototype meets users' needs.
Depending on the type of study, evaluators may be onlookers, participant
observers, or ethnographers. The degree of immersion that evaluators adopt varies
across a broad outsider-insider spectrum. Where a particular study falls along this
spectrum depends on its goal and on the practical and ethical issues that constrain
and shape it.
How to observe
40.2
The same basic data-collection tools are used for laboratory and field studies (i.e.,
direct observation, taking notes, collecting video, etc.) but the way in which they are
used is different. In the laboratory the emphasis is on the details of what
individuals do, while in the field the context is important and the focus is on how
people interact with each other, the technology, and their environment. Furthermore,
the equipment in the laboratory is usually set up in advance and is relatively static
whereas in the field it usually must be moved around. In this section we discuss how to
observe, and then examine the practicalities and compare data-collection tools.
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In controlled environments
The role of the observer is to first collect and then make sense of the stream of
data on video, audiotapes, or notes made while watching users in a controlled
environment. Many practical issues have to be thought about in advance,
including the following.
·  It is necessary to decide where users will be located so that the
equipment can be set up. Many usability laboratories, for example,
have two or three wall-mounted, adjustable cameras to record users'
activities while they work on test tasks. One camera might record facial
expressions, another might focus on mouse and keyboard activity, and
another might record a broad view of the participant and capture body
language. The stream of data from the cameras is fed into a video editing and
analysis suite where it is annotated and partially edited. Another form of
data that can be collected is an interaction log. This records all the user's key
presses. Mobile usability laboratories, as the name suggests, are intended to
be moved around, but the equipment can be bulky. Usually it is taken to a
customer's site where a temporary laboratory environment is created.
·  The equipment needs testing to make sure that it is set up and works as
expected, e.g., it is advisable that the audio is set at the right level to
record the user's voice.
·  An informed consent form should be available for users to read and sign
at the beginning of the study. A script is also needed to guide how users
are greeted, and to tell them the goals of the study, how long it will last,
and to explain their rights. It is also important to make users feel
comfortable and at ease.
In the field
Whether the observer sets out to be an outsider or an insider, events in the field can
be complex and rapidly changing. There is a lot for evaluators to t h i n k about, so
many experts have a framework to structure and focus their observation. Ilk
framework can be quite simple. For example, this is a practitioner's framework that
focuses on just three easy-to-remember items to look for:
·  The Person. Who is using the technology at any particular time?
·  The Place. Where are they using it?
·  The Thing. What are they doing with it?
Frameworks like the one above help observers to keep their goals and questions in
sight. Experienced observers may, however, prefer more detailed frame works, such as
the one suggested by Goetz and LeCompte (19X4) below, which encourages observers
to pay greater attention to the context of events, the people and the technology:
Who is present? How would you characterize them? What is their role?
What is happening? What are people doing and saying and how are they behaving?
Does any of this behavior appear routine? What is their tone and body language?
When does the activity occur? How is it related to other activities?
Where is it happening? Do physical conditions play a role?
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Where is it happening? What precipitated the event or interaction? Do people have
different perspectives?
How is the activity organized? What rules or norms influence behavior?
Colin Kobson (1993) suggests a slightly longer but similar set of items:
·  Space. What is the physical space like and how is it laid out?
·  Actors. What are the names and relevant details of the people involved?
·  Activities. What are the actors doing and why?
·  Objects. What physical objects are present, such as furniture?
·  Acts. What are specific individuals doing?
·  Events. Is what you observe part of a special event?
·  Goals. What are the actors trying to accomplish?
·  Feelings. What is the mood of the group and of individuals?
These frameworks are useful not only for providing focus but also for organizing
the observation and data-collection activity. Below is a checklist of things to plan
before going into the field:
·  State the initial study goal and questions clearly.
·  Select a framework to guide your activity in the field.
·  Decide how to record events--i.e., as notes, on audio, or on video, or using
a combination of all three. Make sure you have the appropriate equipment
and that it works. You need a suitable notebook and pens. A laptop
computer might be useful but could be cumbersome. Although this is
called observation, photographs, video, interview transcripts and the like
will help to explain what you see and are useful for reporting the story to
others.
·  Be prepared to go through your notes and other records as soon as
possible after each evaluation session to flesh out detail and check
ambiguities with other observers or with the people being observed. This
should be done routinely because human memory is unreliable. A basic
rule is to do it within 24 hours, but sooner is better!
·  As you make and review your notes, try to highlight and separate personal
opinion from what happens. Also clearly note anything you want to go back
to. Data collection and analysis go hand in hand to a large extent in
fieldwork.
·  Be prepared to re focus your study as you analyze and reflect upon what
you see. Having observed for a while, you will start to identify interesting
phenomena that seem relevant. Gradually you will sharpen your ideas into
questions that guide further observation, either with the same group or with a
new but similar group.
·  Think about how you will gain the acceptance and trust of those you observe.
Adopting a similar style of dress and finding out what interests the group and
showing enthusiasm for what they do will help. Allow time to develop
relationships. Fixing regular times and venues to meet is also helpful, so everyone
knows what to expect. Also, be aware that it will be easier to relate lo some
people than others, and it will be tempting to pay attention to those who
receive you well, so make sure you attend to everyone in the group.
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Think about how to handle sensitive issues, such as negotiating where you can go.
For example, imagine you are observing the usability of a portable home
communication device. Observing in the living room, study, and kitchen is likely
to be acceptable, but bedrooms and bathrooms are probably out of bounds. Take time
to check what participants are comfortable with and be accommodating and
flexible. Your choice of equipment for data collection will also influence how
intrusive you are in people's lives.
·  Consider working as a team. This can have several benefits: for instance, you can
compare your observations. Alternatively, you can agree to focus on different
people or different parts of the context. Working as a team is also likely to
generate more reliable data because you can compare notes among different
evaluators.
·  Consider checking your notes with an informant or members of the group to
ensure that you are understanding what is happening and that you are making
good interpretations.
·  Plan to look at the situation from different perspectives. For example, you may
focus on particular activities or people. If the situation has a hierarchical
structure, as in many companies, you will get different perspectives from different
layers of management--e.g., end-users, marketing, product developers, product
managers, etc.
Participant observation and ethnography
Being a participant observer or an ethnographer involves all the practical steps just
mentioned, but especially that the evaluator must be accepted into the group. An
interesting example of participant observation is provided by Nancy Baym's work (1997)
in which she joined an online community interested in soap operas for over a year in
order to understand how the community functioned. She told the community what she
was doing and offered to share her findings with them. This honest approach gained her
their trust, and they offered support and helpful comments. As Baym participated
she learned about the community, who the key characters were, how people interacted,
their values, and the types of discussions that were generated. She kept all the
messages as data to be referred to later. She also adapted interviewing and
questionnaires techniques to collect additional information.
As we said the distinction between ethnography and participant observation is
blurred. Some ethnographers believe that ethnography is an open interpretivist
approach in which evaluators keep an open mind about what they will see. Others
such as David Fetterman from Stanford University, see a stronger role for a
theoretical underpinning: "before asking the first question in the field the
ethnographer begins with a problem, a theory or model, a research design, specific
data collection techniques, tools for analysis, and a specific writing style"
(Fetterman. 1998. p. 1). This may sound as if ethnographers have biases, but by
making assumptions explicit and moving between different perspectives, biases are
at least reduced. Ethnographic study allows multiple interpretations of reality; it is
interpretivisit. Data collection and analysis often occur simultaneously in
ethnography, with analysis happening at many different levels throughout the
study. The question being investigated is refined as more understanding about the
situation is gained.
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The checklist below (Fetterman. 1998) for doing ethnography is similar to the
general list just mentioned:
Identify a problem or goal and then ask good questions to be answered by the
study, which may or may not invoke theory depending on your philosophy of
ethnography. The observation framework such as those mentioned above can help
to focus the study and stimulate questions.
The most important part of fieldwork is just being there to observe, as, questions, and
record what is seen and heard. You need to be aware of people's feelings and sensitive
to where you should not go.
Collect a variety of data, if possible, such as notes, still pictures, audio and video, and
artifacts as appropriate. Interviews are one of the most important data-gathering
techniques and can be structured, semi-structured, or open So-called retrospective
interviews are used after the fact to check that interpretations are correct.
As you work in the Held, he prepared to move backwards and forwards between the
broad picture and specific questions. Look at the situation holistically and then from the
perspectives of different stakeholder groups and participants. Early questions arc likely to
be broad, but as you get to know the situation ask more specific questions.
Analyze the data using a holistic approach in which observations arc under stood within the
broad context--i.e., they are contextualized. To do this, first synthesize your notes,
which is best done at the end of each day, and then check with someone from the
community that you have described the situation accurately. Analysis is usually iterative,
building on ideas with each pass.
Data Collection
40.3
Data collection techniques (i.e., taking notes, audio recording, and video recording)
are used individually or in combination and are often supplemented with photos from
a still camera. When different kinds of data are collected, evaluators have to
coordinate them; this requires additional effort but has the advantage of providing
more information and different perspectives. Interaction logging and participant
diary studies are also used. Which techniques are used will depend on the context,
time available, and the sensitivity of what is being observed. In most settings,
audio, photos, and notes will be sufficient. In others it is essential to collect video
data so as to observe in detail the intricacies of w h a t is going on.
Notes plus still camera
Taking notes is the least technical way of collecting data, but it can be difficult
and tiring to write and observe at the same time. Observers also get bored and the
speed at which they write is limited. Working with another person solves sonic of
these problems and provides another perspective. Handwritten notes are flexible in the
field but must be transcribed. However, this transcription can be the first step in
data analysis, as the evaluator must go through the data and organize it. A laptop
computer can be a useful alternative but it is more obtrusive and cumbersome, and its
batteries need recharging every few hours. If a record of images is needed,
photographs, digital images, or sketches are easily collected.
Audio recording plus still camera
Audio can be a useful alternative to note taking and is less intrusive than video. It
allows evaluators to be more mobile than with even the lightest, battery-driven
video cameras, and so is very flexible. Tapes, batteries, and the recorder are now
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relatively inexpensive but there are two main problems with audio recording. One is
the lack of a visual record, although this can be dealt with by carrying a small
camera. The second drawback is transcribing the data, which can be onerous if the con
tents of many hours of recording have to be transcribed: often, however, only sections
are needed. Using a headset with foot control makes transcribing less onerous. Many
studies do not need this level of detail; instead, evaluators use the recording to
remind them about important details and as a source of anecdotes for reports.
Video
Video has the advantage of capturing both visual and audio data but can be intrusive.
However, the small, handheld, battery-driven digicams are fairly mobile, inexpensive
and are commonly used.
A problem with using video is that attention becomes focused on what is seen through
the lens. It is easy to miss other things going on outside of the camera view. When
recording in noisy conditions, e.g., in rooms with many computers running or outside
when it is windy, the sound may get muffled.
Analysis of video data can be very time-consuming as there is so much to take: note of.
Over 100 hours of analysis time for one hour of video recording is common for detailed
analyses in which every gesture and utterance is analyzed.
Indirect observation: tracking users' activities
40.4
Sometimes direct observation is not possible because it is obtrusive or evaluators
cannot be present over the duration of the study, and so users' activities are tracked
indirectly. Diaries and interaction logs are two techniques for doing this. From the
records collected evaluators reconstruct what happened and look for usability and
user experience problems.
Diaries
Diaries provide a record of what users did, when they did it, and what they thought
about their interactions with the technology. They are useful when users are scattered
and unreachable in person, as in many Internet and web evaluations. Diaries are
inexpensive, require no special equipment or expertise, and are suitable for long-term
studies. Templates can also be created online to standardize entry format and enable
the data to go straight into a database for analysis. These templates are like those used
in open-ended online questionnaires. However, diary studies rely on participants
being reliable and remembering to complete them, so incentives are needed and the
process has to be straightforward and quick. Another problem is that participants
often remember events as being better or worse than they really were, or taking more
or less time than they actually did.
Robinson and Godbey (1997) asked participants in their study to record how much
time Americans spent on various activities. These diaries were completed at the end
of each day and the data was later analyzed to investigate the impact of television on
people's lives. In another diary study, Barry Brown and his colleagues from Hewlett
Packard collected diaries form 22 people to examine when, how, and why they capture
different types of information, such as notes, marks on paper, scenes, sounds, moving
images, etc. (Brown, et al.. 2000). The participants were each given a small handheld
camera and told to take a picture every time they captured information in any form.
The study lasted for seven days and the pictures were used as memory joggers in a
subsequent semi-structured interview used to get participants to elaborate on their
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activities. Three hundred and eighty-one activities were recorded. The pictures
provided useful contextual information. From this data the evaluators constructed a
framework to inform the design of new digital cameras and handheld scanners.
Interaction logging
Interaction logging in which key presses, mouse or other device movements are
recorded has been used in usability testing for many years. Collecting this data is
usually synchronized with video and audio logs to help evaluators analyze users'
behavior and understand how users worked on the tasks they set. Specialist software
tools are used to collect and analyze the data. The log is also time-stamped so it can
be used to calculate how long a user spends on a particular task or lingered in a
certain part of a website or software application.
Explicit counters that record visits to a website were once a familiar sight.
Recording the number of visitors to a site can be used to justify maintenance and
upgrades to it. For example, if you want to find out whether adding a bulletin
board to an e-commerce website increases the number of visits, being able to
compare traffic before and after the addition of the bulletin board is useful. You
can also track how long people stayed at the site, which areas they visited, where
they came from, and where they went next by tracking their Internet Service
Provider (LS.P.) address. For example, in a study of an interactive art museum by
researchers at the University of Southern California, server logs were analyzed by
tracking visitors in this way (McLaughlin et al., 1999). Records of when people
came to the site, what they requested, how long they looked at each page, what
browser they were using, and what country they were from, etc., were collected
over a seven-month period. The data was analyzed using Webtrends, a commercial
analysis tool, and the evaluators discovered that the site was busiest on weekday
evenings. In another study that investigated lurking behavior in listserver
discussion groups, the number of messages posted was compared with list
membership over a three-month period to see how lurking behavior differed among
groups (Nonnecke and Preece, 2000).
An advantage of logging user activity is that it is unobtrusive, but this also raises
ethical concerns that need careful consideration (see the dilemma about observing
without being seen). Another advantage is that large volumes of data can be logged
automatically. However, powerful tools are needed to explore and analyze this data
quantitatively and qualitatively. An increasing number of visualization tools are
being developed for this purpose; one example is WebLog, which dynamically
shows visits to websites(Hochheiser and Shneiderman, 2000).
Analyzing, interpreting, and presenting the data
40.5
By now you should know that many, indeed most observational evaluations
generate a lot of data in the form of notes, sketches, photographs, audio and video
records of interviews and events, various artifacts, diaries, and logs. Most
observational data is qualitative and analysis often involves interpreting what
users were doing or saying by looking for patterns in the data. Sometimes
qualitative data is categorized so t h a t it can be quantified and in some studies
events are counted.
Dealing with large volumes of data, such as several hours of video, is daunting,
which is why it is particularly important to plan observation studies very carefully
before starting them. The DECIDE framework suggests identifying goals and
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questions first before selecting techniques for the study, because the goals and
questions help determine which data is collected and how it will be analyzed.
When analyzing any kind of data, the first thing to do is to "eyeball" the data to
see what stands out. Are there patterns or significant events? Is there obvious
evidence that appears to answer a question or support a theory? Then proceed to
analyze it according to the goals and questions. The discussion that follows focuses on
three types of data:
·  Qualitative data that is interpreted and used to tell "the story" about what
was observed.
·  Qualitative data that is categorized using techniques such as content analysis.
·  Quantitative data that is collected from interaction and video logs and
presented as values, tables, charts and graphs and is treated statistically.
Qualitative analysis to tell a story
Much of the power of analyzing descriptive data lies in being able to tell a
convincing story, illustrated with powerful examples that help to confirm the
main points and will be credible to the development team. It is hard to argue with
well-chosen video excerpts of users interacting with technology or anecdotes
from transcripts.
To summarize, the main activities involved in working with qualitative data to
tell a story are:
·  Review the data after each observation session to synthesize and identify
key themes and make collections.
·  Record the themes in a coherent yet flexible form, with examples. While
post-its enable you to move ideas around and group similar ones, they can
fall off and get lost and are not easily transported, so capture the main points
in another form, either on paper or on a laptop, or make an audio recording.
·  Record the date and time of each data analysis session. (The raw data should
already be systematically logged with dates.)
·  As themes emerge, you may want to check your understanding with the
people you observe or your informants.
·  Iterate this process until you are sure that your story faithfully represents
what you observed and that you have illustrated it with appropriate
examples from the data.
·  Report your findings to the development team, preferably in an oral
presentation as well as in a written report. Reports vary in form, but it is
always helpful to have a clear, concise overview of the main findings
presented at the beginning.
Quantitative data analysis
Video data collected in usability laboratories is usually annotated as it is observed
Small teams of evaluators watch monitors showing what is being recorded in a
control room out of the users' sight. As they see errors or unusual behavior, one of
the evaluators marks the video and records a brief remark. When the test is finished
evaluators can use the annotated recording to calculate performance times so they can
compared users' performance on different prototypes. The data stream iron: the
interaction log is used in a similar way to calculate performance times. Typically
this data is further analyzed using simple statistics such as means, standard deviations,
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T-tests, etc. Categorized data may also be quantified and analyzed statistically, as we
have said.
Feeding the findings back into design
The results from an evaluation can be reported to the design team in several ways, as
we have indicated. Clearly written reports with an overview at the beginning and
detailed content list make for easy reading and a good reference document. Including
anecdotes, quotations, pictures, and video clips helps to bring the study to life, stimulate
interest, and make the written description more meaningful. Some teams like quantitative
data, but its value depends on the type of study and its goals. Verbal presentations that
include video clips can also be very powerful. Often both qualitative and quantitative data
analysis are useful because they provide alternative perspectives.
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Table of Contents:
  1. RIDDLES FOR THE INFORMATION AGE, ROLE OF HCI
  2. DEFINITION OF HCI, REASONS OF NON-BRIGHT ASPECTS, SOFTWARE APARTHEID
  3. AN INDUSTRY IN DENIAL, SUCCESS CRITERIA IN THE NEW ECONOMY
  4. GOALS & EVOLUTION OF HUMAN COMPUTER INTERACTION
  5. DISCIPLINE OF HUMAN COMPUTER INTERACTION
  6. COGNITIVE FRAMEWORKS: MODES OF COGNITION, HUMAN PROCESSOR MODEL, GOMS
  7. HUMAN INPUT-OUTPUT CHANNELS, VISUAL PERCEPTION
  8. COLOR THEORY, STEREOPSIS, READING, HEARING, TOUCH, MOVEMENT
  9. COGNITIVE PROCESS: ATTENTION, MEMORY, REVISED MEMORY MODEL
  10. COGNITIVE PROCESSES: LEARNING, READING, SPEAKING, LISTENING, PROBLEM SOLVING, PLANNING, REASONING, DECISION-MAKING
  11. THE PSYCHOLOGY OF ACTIONS: MENTAL MODEL, ERRORS
  12. DESIGN PRINCIPLES:
  13. THE COMPUTER: INPUT DEVICES, TEXT ENTRY DEVICES, POSITIONING, POINTING AND DRAWING
  14. INTERACTION: THE TERMS OF INTERACTION, DONALD NORMAN’S MODEL
  15. INTERACTION PARADIGMS: THE WIMP INTERFACES, INTERACTION PARADIGMS
  16. HCI PROCESS AND MODELS
  17. HCI PROCESS AND METHODOLOGIES: LIFECYCLE MODELS IN HCI
  18. GOAL-DIRECTED DESIGN METHODOLOGIES: A PROCESS OVERVIEW, TYPES OF USERS
  19. USER RESEARCH: TYPES OF QUALITATIVE RESEARCH, ETHNOGRAPHIC INTERVIEWS
  20. USER-CENTERED APPROACH, ETHNOGRAPHY FRAMEWORK
  21. USER RESEARCH IN DEPTH
  22. USER MODELING: PERSONAS, GOALS, CONSTRUCTING PERSONAS
  23. REQUIREMENTS: NARRATIVE AS A DESIGN TOOL, ENVISIONING SOLUTIONS WITH PERSONA-BASED DESIGN
  24. FRAMEWORK AND REFINEMENTS: DEFINING THE INTERACTION FRAMEWORK, PROTOTYPING
  25. DESIGN SYNTHESIS: INTERACTION DESIGN PRINCIPLES, PATTERNS, IMPERATIVES
  26. BEHAVIOR & FORM: SOFTWARE POSTURE, POSTURES FOR THE DESKTOP
  27. POSTURES FOR THE WEB, WEB PORTALS, POSTURES FOR OTHER PLATFORMS, FLOW AND TRANSPARENCY, ORCHESTRATION
  28. BEHAVIOR & FORM: ELIMINATING EXCISE, NAVIGATION AND INFLECTION
  29. EVALUATION PARADIGMS AND TECHNIQUES
  30. DECIDE: A FRAMEWORK TO GUIDE EVALUATION
  31. EVALUATION
  32. EVALUATION: SCENE FROM A MALL, WEB NAVIGATION
  33. EVALUATION: TRY THE TRUNK TEST
  34. EVALUATION – PART VI
  35. THE RELATIONSHIP BETWEEN EVALUATION AND USABILITY
  36. BEHAVIOR & FORM: UNDERSTANDING UNDO, TYPES AND VARIANTS, INCREMENTAL AND PROCEDURAL ACTIONS
  37. UNIFIED DOCUMENT MANAGEMENT, CREATING A MILESTONE COPY OF THE DOCUMENT
  38. DESIGNING LOOK AND FEEL, PRINCIPLES OF VISUAL INTERFACE DESIGN
  39. PRINCIPLES OF VISUAL INFORMATION DESIGN, USE OF TEXT AND COLOR IN VISUAL INTERFACES
  40. OBSERVING USER: WHAT AND WHEN HOW TO OBSERVE, DATA COLLECTION
  41. ASKING USERS: INTERVIEWS, QUESTIONNAIRES, WALKTHROUGHS
  42. COMMUNICATING USERS: ELIMINATING ERRORS, POSITIVE FEEDBACK, NOTIFYING AND CONFIRMING
  43. INFORMATION RETRIEVAL: AUDIBLE FEEDBACK, OTHER COMMUNICATION WITH USERS, IMPROVING DATA RETRIEVAL
  44. EMERGING PARADIGMS, ACCESSIBILITY
  45. WEARABLE COMPUTING, TANGIBLE BITS, ATTENTIVE ENVIRONMENTS