The end-user usually proves to be
the most important asset in any Information Systems design process. IA starts
with users and the reason they come to a site in the first place: they have information
need. IAs need to understand those needs
and behaviors, and their designs should correspond accordingly. There is no
goal more important to design IA than to satisfy users’ needs. Users usually
have two information-seeking behaviors, searching for what you know and
searching for the unknown. Distinguishing between these needs and behaviors and
determining which are your users’ highest priorities is an extremely valuable
pursuit; it helps you determine where to invest your efforts, resources, time
and money as you design your architecture.
Different models exist for what
happens when end-users are seeking information. The “too-simple” model is the
most common, and unfortunately, the most problematic. In this model, the user
asks a question, something happens (searching or browsing), the user receives
an answer, finish. i.e. input, output, end of story. There’s a major problem
here because it rarely happens this way. There are exceptions, e.g. when users
know what they’re looking for and end up having the wrong the information. This
model is also widely disliked because it essentially ignores any context and
narrowly focuses on what happens while the user is interacting with the IA. This
model is dangerous because it’s built upon a misconception that finding
information is a straightforward problem that can be addressed by a simple, algorithmic
approach. The misconception by many user-centered design that the process of
finding is simple enough to be easily measured in a quantifiable way, has led
to the wasting of millions of dollars on search engine software and other
technological remedies that would indeed work if this information were true. The
assumption that the data retrieval challenge has been solved with database
technologies like SQL which use facts and figures is very dangerously
misleading.
Information needs vary amongst
various end-users/information consumers. Database searching seems to be the most
familiar model of searching. However, websites store more than highly
structured data. They store text as the most common data type, and text itself
is made up of ambiguous, messy ideas and concepts, e.g. when we’re seeking
information on the latest happenings within Arsenal FC football club in London,
we’re essentially looking for ideas and concepts that inform us and help us
make decisions, which might actually end up not having an answer, or if there’s
one, is an ambiguous moving target. Sometimes, however users are actually looking
for the right answers, and do actually end up having them, hence the perfect
catch. Other times, users are actually looking for more than just a single
answer, like in the case of Arsenal FC above. They don’t really know what they’re
looking for, and aren’t ready to commit to retrieving anything more than just a
few useful items, or suggestions of where to learn more. This is a typical case
of setting out a lobster trap – you hope that whatever slowly walks in will be
useful, and if it is, that’s good enough. There also comes the time when the
user is actually indiscriminately drift netting, like in the case of
researching for your medical condition or master’s thesis. Here, you want to
catch every sea creature , so you cast you driftnets and drag up everything you
can. This might actually lead to useful consumable information that you’d
prefer never to lose again, and can actually bookmark.
Searching (enter queries in
search systems), browsing (browse from link to link), and asking (through
email, chat interfaces, etc….) are all methods for seeking information, and are
the building blocks of information-seeking behavior. Two other major aspects to
seeking behaviors are integration and iteration. Searching, browsing and asking
are often integrated within the same finding session. You might go through
several iterations within the same finding session since we don’t usually get
things right the first time, or our information needs may change along the way
leading to different approaches with each new iteration. Each iteration of
searching, browsing, asking, and interacting with content can greatly impact
what the end-user is seeking. The various components of the information-seeking
behaviors come together in complex models, such as the “berry-picking” model - users
start with an information need, formulate an information request (a query), and
then move iteratively through an information system along potentially complex paths, picking bits
of information ("berries”) along the way, the “pearl-growing” approach in
which users start with one or a few good documents that are exactly what they
need, they want to get more of the same. Google and many other search engines
allow users to do just that. Corporate portals and intranets of utilize a “two-step”
model, in which users first need to know what they need, they find a good
candidate or two within links and sub- sites, and then perform the second step –
looking for information within those sub- sites.
The IA can actually learn about
their users’ information needs and seeking behaviors by using search analytics
(reviewing the most common search queries on your site, stored in your search
engine’s logfiles as a way to diagnose problems with search performance,
metadata, navigation and content), and
contextual inquiry (uses ethnography, allows you to observe how users interact
with information on their “natural” settings, and, in that context, ask them
why they’re doing what they’re doing). The IA might also consider other
research methods like task analysis, surveys, and might be focus groups.
A better understanding of what
users actually want from your site will definitely help you, as the IA
determine and prioritize which architectural components to build.
Designing web sites for seniors
can be very challenging, as well as also very rewarding. Research has shown
that users aged 65 and older are 43% slower at using web sites than users aged
21-55. In the U.S today, more young users definitely use the web, but the
number of older users is growing very rapidly as a result of an ageing society
and an ever-larger percentage of old people who go online. E-commerce
purchases, online banking and brokerage are a few of the opportunities that
exist in designing for the old. Also, most modern websites ignorantly
discriminate against seniors, so by embracing this population in your design,
you’re actually opening up new avenues for income generation. Some of the
typical design issues the IA might face in incorporating the ageing in their
design is Readability and Clickability since reduced visual acuity is probably
the best-known ageing problem, hence sites should incorporate functions that
let users increase text size as desired, especially if the site’s default is a
normal small font size. Hypertext links are essential design components to ensure
readability and make hyperlinks more prominent targets for clicking. Senior behavioral
issues like hesitation and discouragement actually affect designing the web for
the old and ageing. Research has shown that 45% of seniors show behaviors that
indicated they were uncomfortable trying new things.
On the other side of the
landscape is designing teen-targeted websites. Even though teens tend to be
over-confident in their web
abilities, but they perform worse than adults. Lower reading levels,
impatience, and undeveloped research skills reduce teens’ task success and
require simple, relatable sites. Technology is so integrated with teenagers’
lives that creating useful and usable websites for them is more critical than
ever. Today, the good news is that teens are becoming more successful at
navigating websites and finding what they need. The success rate for teens has
improved 16% during the 8 years between the old and new studies, for an improvement rate of 2 percentage points per
year. This is slightly better than the improvement rate of 1.7% per year for adults using websites over the past decade. However, the bad
news is that although teens might feel confident online, they do make
mistakes and often give up quickly. Fast-moving teens are also less cautious
than adults and make snap judgments; these lead to lower success. Several tweaks
on your website can actually make it more attractive to teens
·
Write well since nothing deters younger audiences more than a
cluttered screen full of text. Teens can quickly become bored, distracted, and
frustrated.
·
Avoid boring content and entertainment overload since teens
usually complain of sites they find boring and dull. Even though they fancy and
appreciate aesthetics, teens detest sites that
appear cluttered and contain pointless multimedia.
·
Make it Snappy – the website should be fast, slow-loading slides
are not for teenagers, period.
·
Avoid condescending statements that talk down to
teens on your site
·
Give teens control over the social aspect of the
site
·
Design for smaller screens and portable gadgets
like tablets, laptops and mobile devices
Designing web information systems for
individuals with disabilities might be a major challenge. Individuals might
experience varying degrees of auditory, cognitive,
neurological, physical, speech, and visual disabilities. For instance, some may
have disabilities from birth, an illness, disease, or accident, or they may
develop impairments with age. Some may not consider themselves to have
disabilities even if they do experience such functional limitations. People
with disabilities access and navigate the Web in different ways, depending on
their individual needs and preferences. Sometimes people configure standard
software and hardware according to their needs, and sometimes people use
specialized software or hardware that helps them perform certain tasks.
Text alternatives are equivalents
for non-text content. Examples include:
·
Short equivalents for images, including icons,
buttons, and graphics
·
Description of data represented on charts, diagrams,
and illustrations
·
Brief descriptions of non-text content such as audio
and video files
·
Labels for form controls, input, and other user
interface components
Text alternatives convey
the purpose of an image or function to provide an equivalent user experience.
Captions and other alternatives for
multimedia
are used by people who cannot hear audio
or see video. Examples of alternatives for audio and video include:
·
Text transcripts and
captions of audio content, such as recordings of people speaking
·
Audio descriptions, which
are narrations to describe important visual details in a video
·
Sign language
interpretation of audio content, including relevant auditory experiences
Well-written text
transcripts containing the correct sequence of any auditory or visual
information provide a basic level of accessibility and facilitate the
production of captions and audio descriptions.
Content can be presented in
different ways for users with specific disabilities. In order for users to be
able to change the presentation of content, it is necessary that:
·
headings, lists, tables, and other structures in the
content are marked-up properly
·
Sequences of information or instructions are
independent of any presentation
·
Browsers and assistive technologies provide settings
to customize the presentation
Meeting this requirement
allows content to be correctly read aloud, enlarged, or adapted to meet the
needs and preferences of the user.
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