Knowledge is Power

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Data, Information, Knowledge, Wisdom, and Foresight

"Knowledge is power." (Sir Francis Bacon, 1561-1626)
"Before you become too entranced with gorgeous gadgets and mesmerizing 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)

The term knowledge is sometimes given a relatively precise definition, and sometimes it is used in a rather broad sense. Thus, the Sir Francis Bacon quote represents a broad definition of knowledge. Similarly, when people talk about an Information overload, they are using the term information is a very broad sense, much as Bacon used the term knowledge.

Five-point, Cognitive Understanding Scale

I find it useful to think about or visualize the terms data, information, knowledge, wisdom, and foresight as a five-point Cognitive Understanding Scale. See the quote from Arthur C. Clarke given above.


Data 5-part.jpeg
Arthur C. Clarke Cognitive Understanding Scale.

Of course, this Cognitive Understanding Scale is not an equal interval scale, and one can argue that the points on the scale are only vaguely related. The left end of the scale corresponds to rote memory with no understanding. The right end of the scale corresponds to having deep understanding, using critical thinking, and doing a careful analysis of the possible consequences of one's actions. When helping a student to learn, we want to help the student learn over the full range of this scale.

However, we tend to test student learning much closer to the left end of the scale than to the right end of the scale. That is cheaper and easier to do—for example, through use of machine scorable tests. In my opinion, this educational focus on the lower order part of the scale represents a major weakness in our current educational system.

Bloom's Taxonomy of Cognitive Learning

Probably you have heard about Benjamin Bloom's six-part taxonomy of cognitive learning. This was developed in 1956, and the definitions of the terms used are somewhat different than the definitions given in the above figure. Quoting from http://www.nwlink.com/~donclark/hrd/bloom.html, the six point scale consists of:

  1. Knowledge: Recall data or information.
  2. Comprehension: Understand the meaning, translation, interpolation, and interpretation of instructions and problems. State a problem in one's own words.
  3. Application: Use a concept in a new situation or unprompted use of an abstraction. Applies what was learned in the classroom into novel situations in the work place.
  4. Analysis: Separates material or concepts into component parts so that its organizational structure may be understood. Distinguishes between facts and inferences.
  5. Synthesis: Builds a structure or pattern from diverse elements. Put parts together to form a whole, with emphasis on creating a new meaning or structure.
  6. Evaluation: Make judgments about the value of ideas or materials.

Notice that the lowest point on the scale is labeled knowledge, but that Bloom' taxonomy takes this to mean data and information that can be memorized and regurgitated with little or no understanding. The key idea is recall, at level lower than comprehension. Bloom's taxonomy has held up well to the scrutiny of scholars during the past 50 years.

Bloom's taxonomy is designed to clearly differentiate between the lowest order (data and information, which he calls knowledge) and the three higher order levels (analysis, synthesis, evaluation) of human cognitive activity. I find it useful to think about the scale as starting at rote memory of data and information with little or no understanding, and ending at the highest level of understanding and critical thinking.

Note added 1/7/2010. The following is quoted from the Website http://www.odu.edu/educ/roverbau/Bloom/blooms_taxonomy.htm:
In 1956, Benjamin Bloom headed a group of educational psychologists who developed a classification of levels of intellectual behavior important in learning. During the 1990's a new group of cognitive psychologist, lead by Lorin Anderson (a former student of Bloom's), updated the taxonomy reflecting relevance to 21st century work. The graphic is a representation of the NEW verbage associated with the long familiar Bloom's Taxonomy. Note the change from Nouns to Verbs to describe the different levels of the taxonomy.
Note that the top two levels are essentially exchanged from the Old to the New version.
Bloom Triangle.jpeg

While Clarke's Cognitive Understanding Scale and Blooms' taxonomy are different scales, they both help to guide the design of formal educational systems. Bloom's taxonomy was developed because colleges and universities were focusing much of their attention at the lower end of the scale. Nowadays, both precollege eduction and higher education are placing increasing emphasis on the higher levels of the two scales.

Lower-Order and Higher-Order

Bloom's taxonomy runs from what are called lower-order cognitive skills to higher-order cognitive skills. It runs from rote memorization with no understanding to critical thinking with a high level of understanding.

Computers, considered as data processing machines, are very good at rote memorization. They far out perform humans. Thus, our educational system is faced by the challenge of computers becoming readily available, and these computers being far better than humans in one of the major components of formal schooling.

Computers, as information processing machines, are quite good over a wide range of somewhat limited tasks. Thus, for example a computer system can gather data from a thermometer, process the data, make a decision as to whether to turn a heating/cooling system on or off, and continue to repeat this sequence over and over again. However, such a computer system has no understanding (in the human sense of understanding) about desirable room temperatures, costs of heating and cooling, effects on global environment, and so on. Some of the decision-making components of the computer program could take into consideration some of the existing human knowledge and understanding of local and global environment. That is, we can make the computer system "smarter" or more "intelligent" in its decision-making process, and still argue that it has no understanding of the area in which it is making decision.

Continuing on the computer-controlled heating and cooling system, think of providing the computer system with more data and control over flow of warmed or cooled air into many different parts of a large building. Suppose that we can provide the computer with data about the outside weather, the number of people in the building and different rooms, how the number of people changes throughout the day, the season of the year (which affects sunlight and the shade from trees and buildings), heating and cooling properties of the building design (properties of walls and windows, for example), and so on. All of this data can be take into consideration by a computer program that controls the heating and cooling system. Then a properly programmed computer system can far out perform a human in making heating and cooling decisions.

In effect, the computer system solves this heating and cooling problem as if it had a high level of cognitive understanding. That is, it provides a good example of a dumb, rote memory, fast data processing machine function in a manner that exceed that of a human with a high level of cognitive understanding. Indeed, in my mind it confuses the differentiation between lower-order and higher-order. A person or a computer system can appear to be doing higher order cognition, when in actuality rote memory and fast processing are at the root of what is occurring.

A somewhat different way of saying this is the high level of cognitive understanding of the computer system designers and programmers, the data collection instruments that feed data to the computer, and data processing capabilities of the computer, and the process control capabilities of the computer all work together to produce good solutions to a very complex problem.

This "dumb machine with little or no cognitive understanding" situation is repeating itself in thousands of different computer applications. A huge number of problem solving and decision making tasks are now being done by computers. Perhaps we do not morn the loss of the elevator operator and telephone switch board operator jobs. However, there is a clear trend, and it is quite important to our informal and formal educational systems. We can look at the technology of the past and the technology of today, and we can make forecasts of the technology of the next few decades. We need an educational system that prepares people for the various aspects of their overall lives in this future.

Computers and the Cognitive Understanding Scale

When the electronic digital computer industry was first developing, it was called the Data Processing Industry. Computers were called data processing machines. In a business, for example, "raw" data from an employee's time card was processed into a measure of hours worked and pay due. That is, data was processed into useful information.

Moreover, the computer system then printed pay checks or deposited money into bank accounts, updated a number of accounting files (of data), and produced summary reports for use by managers.

Over time, the emphasis shifted from the data to the information. Computers came to be thought of as information processing machines. Indeed, a standard definition of a computer came to be: A machine for the input, storage, processing, and output of information.

People understand and accept the idea that a computer can store and process data and information. However, how about the next step up the Cognitive Understanding Scale? For more than 15 years, some people have been thinking about the idea of using computers to process information into knowledge.

This idea meets with resistance. Humans tend to define knowledge in a manner that associates human understanding and knowledge. Many people argue that a computer cannot have (or store, or use) knowledge because it does not have human understanding.

As might be expected, such an argument tends to be emotional and philosophical. It has not stopped the development of a computerized knowledge processing industry. In 1997, the Association for Computing Machinery establish a Special Interest Group on Knowledge Discovery and Data Mining. Its Mission Statement is:

The primary focus of the Special Interest Group on Knowledge Discovery and Data Mining is to provide the premier forum for advancement and adoption of the "science" of knowledge discovery and data mining. To do this, SIGKDD will encourage:
  • basic research in KDD (through annual research conferences, newsletter and other related activities)
  • adoption of "standards" in the market in terms of terminology, evaluation, methodology
  • interdisciplinary education among KDD researchers, practitioners, and users

Continuing to quote from the ACM SIGKDD Website:

Despite all the activities, both in the market and in R&D laboratories, the field is in its infancy. Many of the commercial products do not have the robustness, scalability, and functionality that customers require. Various data-mining algorithms impose serious restrictions on their application. The process of knowledge discovery and data mining is far from automated, and therefore can be quite difficult to use effectively.

Empowering the User

Data, information, knowledge, wisdom, and foresight all empower the user. Thus, computer tools that work with data, information, knowledge, wisdom, and foresight can empower their users.

There is nothing particularly new in this idea. A tool embodies some of the insights of its inventor. A simple tool such as a stabbing spear is sufficiently "transparent" so that if a person views the tool being used, it is easy for the person to understand the purpose, make a version of the tool, and use it reasonably well with relatively little instruction and practice

The same might be said for bow and arrow. However, this is a more complex tool and it takes more knowledge and skill to make a high quality bow and arrow. Moreover, it takes considerably more effort to become skilled in using this tool.

To carry this analysis further, consider fine musical instrument such as a Stradivarius violin. There is a considerable amount of craft knowledge and skill involved in making a fine violin. Moreover, it takes years of instruction, study, and practice to develop a high level of skill in using the instrument.

In summary, humans have a very long history of developing and using tools. Some tools are both simple to manufacture and easy to learn to use. Some are both very difficult to manufacture and very difficult to learn to use well. In either case, a person can be empowered by learning to use a tool, and a person can be empowered to learn to manufacture a tool or to have knowledge and skills that produce trade goods/money to buy a tool.

Now, let's apply this line of thought to a computer and a variety of computer tools. A computer itself is quite complex. A person cannot look at a computer or someone using a computer, and have good insight into how to design and construct a computer. Moreover, a computer system is both hardware and software. A piece of software such as a word processor or a search engine incorporates the knowledge and skills of a large number of software designers, programmers, and testers working together over a long period of time.

Interestingly, a third grade child can readily learn to use a word processor or a search engine. The knowledge and skills needed to do this are reading and writing. Computer hardware and software constitute a new type of tool. While many pieces of software are quite complex and not easy to learn how to use, many others are quite easy to learn how to use at a personally useful level.

Let's think a little more about a word processor. A third grader can read and write well enough to make effective use of a word processor. Moreover, with some instruction and practice, a third grader can learn fast keyboarding, and so can keyboard faster than he or she can hand print or hand write. A word processor "empowers" the young writer. It solves the problem of producing legible text, it helps with spelling, grammar, and editing.

By itself, however, a word processor does little to make a young student into a better writer. As with a violin, the tool does not make the expert. Rather, the expert makes use of the tool, and a better tool contributes to the products and performances of the expert. A person can be a good writer whether or not they make use of a word processor.

Our educational system is struggling with this distinction. As a simple example, there have been many research studies exploring whether learning to use a word processor makes a person into a better writer. A word processor is a great tool. However, if we want students to get better at writing, let's do a better job of teaching writing. Better yet, lets teach writing in a word processing environment, so we can combine the power of the tool with our collective understanding of how to become a better writer.

Educational Implications

The discussion given above presents the idea that data, information, knowledge, wisdom, and foresight all empower a person. Tools can incorporate various aspects of these Cognitive Understanding Scale ideas. A person who learns to make effective use of such tools tends to have physical and/or cognitive advantage over a person who doesn't have access to the tools and the knowledge and skills to effectively use the tools.

The totality of data, information, knowledge, wisdom, and foresight is huge and growing very rapidly. Tools to aid in the gathering, storage, processing, retrieving, and using this totality will continue to get better and better. We are already very dependent on these tools, and we will become more and more dependent in the future.

A good education prepares a person to function in a changing environment that brings new and challenging problems. Our current educational system can be improved by placing less emphasis on the lower ends of the Blooms' Taxonomy and the Clarke scales, and more emphasis on the higher ends. Place much more emphasis on learning to work with the tools and in adjusting to a steady stream of newer, more capable tools. Recognize that this is a type of empowerment—supplementing both the physical and mental capabilities of people.

References

Churches, Andrew (5/1/2008). Bloom's Taxonomy Blooms Digitally. Retrieved 1/8/2010 from http://techlearning.com/article/8670. Here is a revised version of the 1956 Bloom's Taxonomy quoted from the Website:

  • Remembering - Recognising, listing, describing, identifying, retrieving, naming, locating, finding
  • Understanding - Interpreting, Summarising, inferring, paraphrasing, classifying, comparing, explaining, exemplifying
  • Applying - Implementing, carrying out, using, executing
  • Analysing - Comparing, organising, deconstructing, Attributing, outlining, finding, structuring, integrating
  • Evaluating - Checking, hypothesising, critiquing, Experimenting, judging, testing, Detecting, Monitoring
  • Creating - designing, constructing, planning, producing, inventing, devising, making

Gardner, Howard. Video: Five Minds of the Future. A five minute video available at http://www.uknow.gse.harvard.edu/teaching/TC106-607.html. Quoting from the website:

… the reflective educator can think about three kinds of minds that emphasize various aspects of intellectual development. Howard Gardner describes what it means for citizens and workers to exhibit these types of minds.

The video is based on Howard Gardner's book by the same title. Quoting from the Website:

As the world we inhabit continues to change, educators must frequently reevaluate the goals of education, and the type of "minds" we wish to cultivate. Though academic achievement within the disciplines is an important goal for K-12 education, there are other important components of a future-oriented education.
Howard Gardner, professor of cognition and education at Harvard Graduate School of Education, points out that the future will demand workers and citizens to demonstrate "out-of the box" and non-linear thinking to solve increasingly complex challenges. The tools from any one discipline are often insufficient for understanding and solving real world problems. For example, in the medical and educational arena, complex syndromes such as autism are highlighting the need for interdisciplinary expertise and problem-centered teams of people working on common goals.

Minimalism in Education.

Two Brains Are Better Than One. Zverina, Jan (12/16/08). The Economics of Data Preservation: International Blue Ribbon Task Force Issues Interim Report on Economic Issues Brought on by “Data Deluge” in the Information Age. University of California San diego. Retrieved 12/23/08: http://ucsdnews.ucsd.edu/newsrel/general/12-08BRTF.asp.

Quoting from the article:

A blue ribbon task force, commissioned late last year to identify sustainable economic models to provide access to the ever-growing amount of digital information in the public interest, has issued its interim report. The report calls the current situation urgent, and details systemic pitfalls in developing economic models for sustainable access to digital data.
A recent study by the International Data Corporation (IDC) said that in 2007, the amount of digital data began to exceed the amount of storage to retain it, and will continue to grow faster than storage capacity from here on. The IDC study predicts that by 2011, our “digital universe” – consisting of digitally-based text, video, images, music, etc. – will be 10 times the size it was in 2006.

Author or Authors

The first version of this page was created by David Moursund.