This free Information Age Education
is written by Dave Moursund and Bob Sylwester, and produced by Ken
Loge. The newsletter is one component of the Information Age
Education (IAE) publications.
All back issues of the newsletter and subscription information are
In addition, four free books based on the newsletters are
available: Understanding and
Mastering Complexity; Consciousness
and Morality: Recent
an Appropriate 21st Century Education;
and Common Core State
Standards for Education in
This is the third IAE Newsletter
in a new series devoted to the educational issue of credibility and
validity of information.
Credibility and Validity of Information
Part 3: Information Overload and Underload
Emeritus Professor of Education
University of Oregon
“What is the use of
having countless books and libraries, whose titles their owners can
scarcely read through in a whole lifetime?” (Lucius Annaeus Seneca;
Roman Stoic philosopher, statesman, dramatist; 4 BC-65 AD.)
The previous two articles in this series suggested that future students will need to become much more personally responsible for determining the credibility and validity of information that they use. Quoting from the first of these newsletters:
Credibility focuses on a belief
that the person who made an allegation about a phenomenon is believable
and can indeed be trusted. It is common to talk about a person and what
the person writes/says as being credible and believable.
Validity is an important
component of educational research. The word tends to be used in two
somewhat different ways:
Validity is the quality of being logically or factually sound.
Validity is the extent to which a concept, conclusion, or measurement
A research instrument or test is valid if it measures what it is
purported to measure.
In brief summary, one can think of credibility having a subjective base
and validity having an objective base.
This IAE Newsletter discusses
information overload and underload (Moursund, 2014). The quote given at
the beginning of this newsletter indicates that the concept of
information overload is a
couple of thousand years old. My 11/2/2014
Google search of information
overload underload produced over 54
A Digital Camera Example
I have a relatively inexpensive 16-megapixel camera. I use it to take a
picture. The result is approximately 32 million bytes (256 million
bits) of data. In one click of my camera I produce about 256 million
zeros and ones. If my camera happens to be in video mode and I take
five seconds of 24 frames/second video, I produce over 30 billion bits
You have probably heard the statement, “A picture is worth a thousand
words.” Actually, the text in a 250-page novel requires less than a
million bytes of computer storage. So, in some sense a color picture is
worth 32 books!
If those numbers don’t completely overwhelm you, then consider the
Large Hadron Collider (LHC). When an experiment is
being run, the
equipment takes about 40 million pictures per second. One second of
data from the LCD is roughly equivalent to 13 years of high definition
television or several hundred million books. That is a lot of data!
Knowledge, Wisdom, and Foresight
“Before you become too
entranced with gorgeous gadgets and
video displays, let me remind you that information is not knowledge,
knowledge is not wisdom, and wisdom is not foresight. Each grows out of
the other, and we need them all.” (Arthur C. Clarke; British science
fiction author, inventor, and futurist; 1917-2008.)
The following diagram expands on Clarke’s statement and provides some
People often use the term information
to include all five of the
categories data, information, knowledge, wisdom, and foresight. Within
any discipline of study we can inquire about the credibility and
validity of its information.
The digital camera example of the previous section certainly indicates
that we have data overload. Fortunately, we can use computers and other
automated machines to process data. My digital camera snapshot can be
made into a printed color photograph for about 15 to 20 cents or
so. Then I can view and appreciate the picture, share it with friends,
and save it in a photograph album. I am dealing with one printed
photograph, rather than with 32 million bytes of data.
A Tidbit of Computer History
The first commercially-produced electronic digital computer was
named the UNIVAC and became available in June, 1951. In
those days, computers were considered to be data processing machines. Gradually,
computers began to be regarded as information processing machines. In
my early studies in Computer Science, I learned that “A computer is a
machine for the input, storage, processing, and output of information.” Over time, the
discipline of Computer Science was renamed Computer and Information
The field of computer and information science has grown substantially
as computers have steadily become more capable. You may be surprised to
learn that the Association for Computing Machinery held its 20th annual
meeting on Knowledge and Data Mining August 27, 2014. The
2011 success of IBM’s computer named Watson in defeating human
players of the TV game Jeopardy has made it clear that computers are
now quite powerful knowledge
We now think of a computer as a machine for the input, storage,
processing, and output of data, information, and knowledge. The wisdom
and foresight still must come from its users. Computer scientists and
others continue to struggle with the nature and extent of current
computer intelligence and the idea that eventually computers may (will)
surpass humans in intelligence.
"Everybody gets so much
information all day long that they lose their common sense." (Gertrude
Stein; American writer, poet and feminist; 1874-1946.)
I remember the “good old days” when my
wife and I bought the Encyclopedia
Britannica. This seemed like a large investment at the time, and
I built a special bookcase just to house this collection.
Now, the Wikipedia is available free on the Web. Quoting from the Wikipedia (Try not to laugh. I am quoting the
Wikipedia about itself. Is this information credible and valid?):
The English Wikipedia alone has over
2.6 billion words, over 100 times as many as the next largest
English-language encyclopedia, Encyclopædia
I routinely use Google to search the Web. Quoting from John Koetsier
[A Google] search starts, of course,
with crawling and indexing, and Google says that the web now has 30
trillion unique individual pages. That is up an astonishing 30 times in
five years: Google reported in 2008 that the web had just one trillion
Google says that it stores information about those 30 trillion pages in
the Google Index, which is now at 100 million gigabytes. That’s about a
thousand terabytes, and you’d need over three million 32GB USB thumb
drives to store all that data.
When you search, Google tries to figure out not just what you’re typing
into the box, but what you mean. So algorithms for spelling,
autocompletion, synonyms, and query understanding jump into action.
When Google thinks it knows what you want, it pulls results from those
30 trillion pages and 100 million gigabytes, but it doesn’t just give
you what it finds.
First, a ranking procedure uses over 200 closely guarded secret factors
that look at the freshness of the results, quality of the website, age
of the domain, safety and appropriateness of the content, and user
context like location, prior searches, Google+ history and connections,
and much more.
This last sentence is particularly important. In essence, it says that
Google “screens” the hits it provides you so that the ones it considers
will be most useful to you are at the top of the list. As Google learns
more about you, it uses this knowledge in the screening process.
You might think that a Web search will answer any question you can
think of and locate any information that you desired to find. But, that
is not the case. Information underload is usually used to describe the
situation in which a person cannot gain access to desired information
that is known to exist. But, how does a person know what information
I think it is better to think of information underload in terms of a
Not knowing what information
exists. When I pose a question or problem to the Web, it would
be nice if the Web had enough intelligence to be able to tell me if, as
of yet, there is no known answer to the question or problem. The
information retrieval system could then go on to explain promising
areas of research and development that are making progress in this
area, and provide me with access to information about this progress.
Not being able to retrieve some
of the information that is known to exist. Much of the
accumulated knowledge of the human race is not (yet) online, and much
that is online is only available for a fee. In addition, there is much
information that is proprietary or “secret.” I want the information
retrieval system to inform me about such situations and why I cannot
have access to the information that I want.
Not having the knowledge and
skills to make effective use of the information that one retrieves.
A good information retrieval system would have provisions for me to
tell it my informal and formal educational background, my interests,
and other information that would help the system to provide me with
information at a level I can understand. (If I have just a high school education, I likely don’t want an answer designed for Ph.D. research Biologists!) In the future, a good information retrieval system will
also be a teaching machine. When I am exploring a topic, the system
will apprise me of online instructional materials designed to help me
learn more about the topic I am exploring.
The total accumulation of information is huge and is growing rapidly.
We suffer from both information overload and information underload.
Broad-based browsers such as Google do not screen websites for content
credibility and validity. Indeed, they don’t even carry a warning, sign
such as: Readers beware! This browser
does not assume responsibility for the correctness of the website
information it helps you locate.
Here are some important points to consider:
As we search for needed information, we are faced by the problem
of “Garbage in, garbage out” (GIGO). Much incorrect and/or very biased
information is integrated into the total collection of information
available to us. Even an expert in a particular small domain can have
trouble separating the wheat from the chaff in his or her domain of
expertise. It is not at all surprising that ordinary people often are
so easily mislead by what they read on the Web. Our educational system
is weak in helping students learn to assess the credibility and
validity of information sources and the information they provide.
It takes substantial education and experience to make effective
use of much of the accumulated information that is available. Contrast
the current Web with a “smart” Web-of-the-future that provides answers
suitable to the knowledge, experience, and contextual situation of the
individual posing a question or problem. We have a very long way to go
to provide this level of individualization.
Think about the difficulty of communicating a problem or question
to a computer so that it retrieves just a quite limited number of good,
useful answers. When I do a Web search, I am quite displeased when I get thousands or
millions of hits. This problem can be addresses in two ways. One way is
to provide students with substantial instruction and practice in
communicating problems and questions to a computer system, and in
refining their communication if it does not produce results that meet
their needs. A second way is to make the information retrieval systems
smarter. Progress in artificial intelligence is gradually doing this.
David Moursund earned his doctorate in mathematics from the
University of Wisconsin-Madison. He taught in the departments of
Mathematics, Computer Science, and Teacher Education at the University
of Oregon. A few highlights of his professional career include founding
the International Society for Technology in Education (ISTE), serving
as ISTE’s executive officer for 19 years, and establishing ISTE’s
flagship publication, Learning and
Leading with Technology (now named Entrsekt). He was a major professor
or co-major professor of 82 doctoral students. He has authored or
coauthored more than 60 academic books and hundreds of articles. He has
presented hundreds of professional talks and workshops.
We are using the Disqus commenting system to facilitate comments and
discussions pertaining to this newsletter. To use Disqus, please
click the Login link below and sign in.
If you have
questions about how to use Disqus, please refer to this help