Knowledge is Power
From IAE-Pedia
Contents |
[edit] 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. Clark)
The term knowledge is sometimes given a relatively precise definition, and sometimes it is used in a rather broad sense. I do both when writing about Information and Communication Technology (ICT) in education.
I find it useful to think about or visualize the terms data, information, knowledge, wisdom, and foresight as a five-point 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.
Perhaps 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:
- Knowledge: Recall data or information.
- Comprehension: Understand the meaning, translation, interpolation, and interpretation of instructions and problems. State a problem in one's own words.
- 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.
- Analysis: Separates material or concepts into component parts so that its organizational structure may be understood. Distinguishes between facts and inferences.
- 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.
- 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 (knowledge; data and information) and the higher order (analysis, synthesis, evaluation) of human cognitive activity. I find it useful to think about the scale as staring at rote memory of data and information with little or no understanding, and ending at the highest level of understanding and critical thinking.
While the 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.
[edit] 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 on a furnace or an air conditioner, 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.
Continuing on the computer-controlled heating and cooling system, think of providing the computer system with more data. Suppose that we can provide the computer with data about the outside weather, the number of people in the building, 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. Our educational system tends to support this type of learning through giving tests that focus strictly on what has been covered in the lectures and the readings.
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.
[edit] 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.
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? In recent years, people have begun to think 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.
[edit] 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 skills 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.
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 writer learning to use a word processor makes a person into a better writer. A word processor is a great tool. If we want students to get better at writing, let's teach 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.
[edit] 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, such as the one I am forecasting.
[edit] References
[edit] Author or Authors
The first version of this page was created by David Moursund.
