Computers in Education: A Brief History

The history of computers in education has been variously characterized as an "accidental revolution" or "unthinking man and his thinking machines." Others have said that the computer revolution has changed the adage that "necessity is the mother of invention" to "in a computer world, invention is the mother of necessity." However characterized, it is clear that innovators in this field have created some of the most provocative and stimulating ideas in the history of education. What follows is a brief chronological history of some of the more interesting ideas and developments.


Broadly speaking, the two major functions of education are to transmit the culture, values and lessons of the past to the current generation; and to prepare our children for the world in which they will live. Preparing children for the world in which they will live is becoming more difficult than ever. In retrospect, there has been a confluence of changes that have significantly impacted the direction of modern education.

1. The Global Economy

Modern, high-speed computers and telecommunications have facilitated the rapid movement of financial resources, goods and services, and have created an interdependence among the worldís economies. To benefit from these markets, nations must be competitive, and to be competitive they must have a well-educated work force.

New, science-based, information industries are emerging in which knowledge and human capital are as important as industrial plants. Daniel Bell says a major characteristic of these industries is that they derive from work in theoretical science and are dependent on the codification of theoretical knowledge. The significance of this development is that if we choose to maintain our current standard of living, our knowledge workers must compete in an international market and must have a good understanding of science.[1]

2. The Scientific Information Explosion

We are experiencing a scientific information explosion of unprecedented proportions. Today, scientists and engineers use computers to access thousands of rapidly growing data bases that store numbers, words, maps, chemical and physical structures; and they search them millions of times a year. The base of scientific knowledge today is huge. It is estimated that it would take 22 centuries to read the annual biomedical research literature or seven centuries to read a yearís chemical literature.[2]

Not only is the volume of new information large, but it is growing exponentially. Rapid changes in many fields are making basic knowledge and skills obsolete. Knowledge is continually being modified and basic concepts and theories are being revised. New theories emerge as new discoveries offer new ways of looking at the data. Disciplines are merging and hyphenated sub-disciplines are being formed.

Herbert Simon, Nobel Laureate, observed that the developments in science and information processing technologies have changed the meaning of the verb, "to know." It used to mean "having information stored in oneís memory." It now means the process of having access to information and knowing how to use it.[3] 

3. The Emergence of Cognitive Science

There has been a major paradigm shift in education from theories of "learning" to theories of "cognition." Cognitive science approaches teaching and learning in a different way. It addresses how the human, as an information processor, functions and uses information. Rather than focusing on teaching facts through expository lectures or demonstrations, the emphasis is, instead, on developing higher-order, thinking and problem-solving skills.

The cognitive approach is important because it recognizes human information processing strengths and weaknesses, and the limits of human perception and memory in coping with the information explosion. It focuses, instead, on organizing information to fit human capacity, and has changed the emphasis in education from learning to thinking.[4,5]

4. New Educational Demands

The launching of Sputnik, an unmanned Soviet satellite, in 1957 stirred national interest in educational reform. Thus began what has been called the "golden age" of education. Major national efforts were made to reform education.

While many of the problems in education were not new, other new and different demands were changing the basic structure of education. First, there was a change in national philosophy from a position of making mass education available to many to a challenge to provide education for all. Second, we were preparing children for a new type of society that did not yet exist.

Third, since people were now living significantly longer, formal education could not end with a high school or even a college degree. Itís estimated that workers would have to prepare for two to three career changes in their lifetime. Fourth, modern communications such as radio, film, television and computers had created an information-rich society. Schools were no longer the only center of information, but had to compete for student attention. Finally, the new emerging educational technologies were to become an important catalyst for rethinking education.


The history of the modern computer age is a brief one. It has been about 50 years since the first operational computer was put into use: the MARK 1 in 1944 at Harvard and ENIAC in 1946 at the University of Pennsylvania. Early use of computers in education was primarily found in mathematics, science and engineering as a mathematical problem-solving tool, replacing the slide rule and thus permitting students to deal more directly with problems of a type and size most likely to be encountered in the real world.[6]

In 1959, at the University of Illinois, Donald Bitier began PLATO, the first, large-scale project for the use of computers in education. The several thousand-terminal system served undergraduate education as well as elementary school reading, a community college in Urbana, and several campuses in Chicago.[7] Thus, the era of computers in education is little more than 35 years old.[8]

The Early Pioneers

At Dartmouth, in 1963, John Kemeny and Thomas Kurtz transformed the role of computers in education from primarily a research activity to an academic one. They did not like the idea that students had to stand in long lines with punch cards for batch processing. So they adopted the recently demonstrated concept of time-sharing that allowed many students to interact directly with the computer. The university developed the time-shared system and expanded it into a regional computing center for colleges and schools.[9] At the time, most programs were written in machine language or FORTRAN. Kemeny and Kurtz developed a new, easy-to-use language, called BASIC. It spread rapidly and was used for the creation of computer-based instructional materials for a wide variety of subjects and for all levels of education.

Computer-Assisted Instruction

In 1963, while at Stanford, Patrick Suppes and Richard Atkinson established a program of research and development on computer-assisted instruction in mathematics and reading. They sought to free students from the lock-step process of group-paced instruction and developed individualized, instructional strategies that allowed the learner to correct his responses through rapid feedback. The self-paced programs allowed a student to take an active role in the learning process. Mastery was obtained through drill-and-practice.[10]

Micro Worlds

In the early seventies at MIT, Seymour Papert set out to develop a new and different approach to computers in education. He developed a programming language, LOGO, to encourage rigorous thinking about mathematics.

He wanted it to be accessible to children and to be easy to express procedures for simple tasks like many non-numerical problems familiar to children. He used it to teach mathematics by teaching LOGO in a wide variety of interesting "micro world" environments such as music and physics. Papert insisted that we should not teach mathematics, but should teach children to be mathematicians. LOGO soon became the language of the elementary school computer literacy movement.[11]

Later, Papert extended LOGO to work with LEGO construction kits. The Constructivist approach viewed learning as a reconstruction of knowledge. Papert asserted that learning is more effective when the learner actually constructs a meaningful product. In building computer-driven LEGO constructions, the student learns to define a problem and the tacit practical problem-solving skills needed to solve it. Papert has tried to move education from "computer literacy," an appreciation of computing, to "computer fluency," the application of computers to solve real problems.[12]


In the late 1960s, in order to make access to computers widely available, the National Science Foundation (NSF) supported the development of 30 regional computing networks, which included 300 institutions of higher education and some secondary schools. By 1974, over two million students used computers in their classes. In 1963, only 1% of the nationís secondary schools used computers for instructional purposes. By 1975, 55% of the schools had access and 23% were using computers primarily for instruction.[13]

The Microcomputer

Initially, because computers were expensive, educators purchased time-shared systems and adopted procedures to ration or restrict usage to provide access to as many people as possible given limited resources. In 1975 a remarkable thing happened, the economics that once favored large, time-shared systems shifted to low-cost microcomputers and the personal computer revolution began.

By the late seventies personal computers were everywhere -- at the office, the schoolroom, the home, and in laboratories and libraries. The computer was no longer a luxury, but was now a necessity for many schools and universities. Many universities required incoming freshmen to own a computer. What began as a grassroots revolution driven by students, teachers and parents, was now a new educational imperative as important as having books and libraries.


James Kulik at the University of Michigan performed a meta-analysis on several hundred well-controlled studies in a wide variety of fields at the elementary, secondary, higher- and adult-education level. He found that computer-based education could increase scores from 10 to 20 percentile points and reduce time necessary to achieve goals by one-third. He found that computers improved class performance by about one-half a standard deviation, less than the one sigma difference that could be accomplished by peer tutoring.[14] However, this analysis did not include newer studies utilizing advanced technologies and newer educational paradigms. But, this study did answer the question: do computer-based technologies work? They most certainly do. 

Intelligent Tutors

In the 1970s, researchers were looking for new educational paradigms to take advantage of breakthroughs in computer technology. It appeared that the combination of artificial intelligence, cognitive science and advanced technologies could dramatically improve learning and problem solving. Intelligent CAI (ICAI) was one such paradigm.

John Seely Brown developed SOPHIE (a SOPHisticated Instructional Environment) as a new kind of learning environment in which the computer-based instruction system literally understands its subject domain and can use its knowledge base to help the student debug and articulate his own ideas and reasoning strategies.[15]

John Anderson of Carnegie Mellon University developed a theory of cognition (ACT) and developed ICAI tutors in algebra, geometry and teaching computer programming languages. Andersonís goal was to achieve a one sigma difference in school performance. Results show a one letter-grade improvement for all students.[16]

Intelligent Tools

Another approach was to build the intelligence into the tool. If educators were to raise the entire level of educational performance, merely learning faster or better would not be sufficient. Often the difficulty in problem solving is not inherent in the nature of the problem, but in the tools available to us. Computer-aided design and computer-aided manufacturing in engineering greatly empowered students to achieve what professional engineers were able to accomplish using the older methods. The aim was to build the intelligence into the tool and let students focus on problem-solving and reasoning.

In another example of computer-aided performance, Wallace Feurzeig of BBN developed an algebra workbench that has the intelligence to solve algebra problems as directed by the student. The purpose was to create a rich exploratory environment and introduce intelligent aids and computer tools to shift the focus of instruction away from manipulative skills, something the computer d'es well, to an emphasis on qualitative reasoning and problem solving. [17]


Doing better is not the same as doing better things. Increasingly, many concepts and ideas cannot be taught without the aid of technology to represent and manipulate them. As a tool, high-speed computers revolutionized the representation and manipulation of information. Computers became the new instruments for extending our senses and intuition. Computer models, simulations and other symbolic representations provided an environment for the vicarious learning necessary to build human intuition. Modern science has, after all, outstripped sensory experience. The new world of science was about abstractions and complexity. Andrea DiSessa says the trick is not to turn experiences into abstractions with a computer, but to turn abstractions, like the laws of physics, into experiences.[18]

New visual metaphors are needed to express abstract, dynamic, non-linear concepts. For example, it is recognized that nature is full of something called "deterministic chaos" or physical systems that obey deterministic laws at one level, but behave unpredictably at another. Weather patterns, turbulence in air currents, or the flow of liquids are examples of phenomena that cannot be easily represented without computer techniques.

Mathematicians and scientists, as another example, found that traditional Euclidean geometry was not adequate to represent many concepts in nature. Fractal geometry was created to provide mathematical descriptions of irregular and complicated phenomena such as the shapes of mountains and clouds and how galaxies were distributed in the universe. Computer graphics are necessary to represent the various fractal forms.

Integrating new important developments such as theories of fractals, chaos and complexity into the curriculum traditionally takes about 20 to 30 years. Eugene Stanley at Boston University has attempted to create a new model for combining scientific research and education with the aim of shortening the long lead time for incorporating new concepts into the educational process.

Stanley and an international group of researchers, who are conducting research on chaos and fractal geometry in science, have created models for graduate students on a supercomputer. With the aid of these students, they have created lessons that can be downloaded for use on smaller RISC machines for undergraduate education. Computer networks are being used to deliver the models and concepts into high schools. Thus, new theories and concepts are being introduced at all levels of education in a coherent and articulated manner and are made available through a hierarchical, computer network. [19]


The increased complexity in science exceeds human capability. Marshall McLuhan says we are witnessing a revolution that is totally new and is changing the very nature of human perception and experience. He says the computer and television have literally moved us into the world of pattern recognition and out of the world of mere data collection. [20]

Humans have difficulty in handling problems that involve large quantities of data or have many interrelated structures. Today, information overload is a fact of life and while it is not possible to meaningfully eliminate complexity, it is possible to manage it.

1. Symbol Systems

New symbol systems have enabled scientists and mathematicians to make dramatic breakthroughs. The use of Arabic numerals instead of Roman numerals greatly changed mathematics. The computer spreadsheet in business is another such invention. The computer d'es not need a spreadsheet to operate, it is a mental model to facilitate human-machine interaction. Such techniques as symbolic, iconic, visual and functional programming are being applied in new problem-solving approaches for ease of representation and manipulation.

2. Visualization

Computer graphics and visualization techniques are used to overcome complexity and the limits of the written word. It is said that computer visualization not only changes how we see phenomena, but also how we think about them. It is believed that it restructures a problem and shifts more work to our perceptual systems thus freeing the brain for higher levels of analysis and synthesis and thus approaches the speed of thought. The computer restructures the problem so that it may more easily be processed by the human visual and perception systems (see photo, right). [21]

Biologists used to depend on the microscope and dissection to examine an organism. Today, they gain understanding by using a supercomputer to visualize and animate dynamic processes in developmental and microbiology. Students observe complex biological patterns as they develop. These dynamic, complex concepts are extremely difficult to portray using words, equations, graphs or static pictures.

3. Virtual Reality

Spurred by the advent of digital video systems and robotics, virtual reality is emerging as a new computational paradigm for creating mediated experiences. The learner interacts with electronically generated artificial environments as if they were real and develops experiential knowledge.

Hypothetical environments permit the learner to see the behavior of objects as they approach the speed of light or let them take a vicarious trip through the human circulatory system. The aim is to create a deeper, intuitive understanding of phenomena that lie outside human perception. It provides a direct, non-verbal way to communicate scientific information.


Many universities are pursuing virtual degree programs, offering classes via the Internet. They utilize its two-way digital video features to allow students with personal computers and teachers to interact, one-on-one, or in classes that may be many miles apart.

Universities such as the University of Maryland and Duke University are offering Masters degree programs entirely through the Internet. Education is now a flexitime, flexiplace activity.

Workers in many industries require a new and different type of training. Education and training, as conventionally practiced, fail to provide many individuals with the level of understanding and skills to be productive contributors in a world of fast-changing technology, says Barbara Means of SRI International. She is experimenting with the concept of distant mentoring as an alternative instructional tool that can bridge the gap between concepts stressed in formal education and the competencies required by the workplace. She has developed mentoring and advisory systems for use in high-technology industries. [22]

2. Learning-on-Demand

Gerhard Fischer of the University of Colorado is developing knowledge-based environments to support learning-on-demand. As job content changes rapidly in the new science-based information world, new pedagogies are needed to upgrade oneís knowledge and to develop skills that answer current and immediate problems on the job. [23]

3. Organizational Learning

The recent explosion of use of intranet facilities in the work environment has created a new awareness of the need for organizational learning. In complex work environments, workers must work collaboratively with other people and must also utilize the specialized knowledge of others in order to do their jobs. New organizational infrastructures are being formed to greatly enhance and extend what any single individual or organization can accomplish.

In the future, continuing education will require more than just additional courses. Learning and work are becoming indistinguishable. A new role for education will be to broadly gather information from distributed sources and provide it, on-demand, to individuals and organizations as they require it.


In the 1980s, supercomputers appeared on the scene. Supercomputers permitted the solution of previously intractable problems and the discovery of new phenomena. The merging of powerful computers with high-bandwidth communication networks made it possible, through distributed technologies, to allow global access to knowledge and information anywhere in the world.

This has greatly expanded access to information and increased the speed with which ideas are disseminated. It produces a new form of knowledge, an "infosphere," based on the interaction of people, information, technology and new social organizations. This evolving infrastructure will inevitably lead to a major restructuring of education.

In 1984, NSF established five supercomputer centers and connected them with a high-bandwidth backbone so that computers could talk to computers. In 1985, NSF built a national network, NSFNET, to make large systems available to all colleges and universities for research and education. It now links over 1,500 networks and well over 100,000 computers and over one million users all over the world. [24]

There is now a government-wide effort to dramatically expand the U.S. portion of the Internet. The aim is to interconnect the nationís educational infrastructure to its knowledge and information centers. Elementary and high schools, colleges and universities will be linked to research centers and laboratories so that all may share access to digital libraries, databases and diverse scientific instruments. [25]


In about 1987, Robert Tinker and his staff at TERC helped develop the National Geographics KidsNet: a new, innovative way to bring inquiry-based learning to elementary school children. These students perform experiments on such topics as acid rain and water quality. They gather data; analyze trends and patterns on topics of current scientific, social and geographic interest; and communicate with each other and with practicing scientists using electronic mail. They send the results of their local experiments to be combined with national and international results.

There were several significant instances in which the childrenís tests led to the discovery that school drinking water and air pollution standards were not being met. In 1991, KidsNet units were used in more than 6,000 classrooms in 72 countries. More than 90% of teachers using KidsNet reported that it significantly increased studentsí interest in science, and that their classes spent almost twice the amount of time on science than they otherwise did. [26]

In another network activity, Tinker created a Global Laboratory Network. Low-cost devices for measuring ozone, soil moisture and ultraviolet radiation were developed to measure the effects of global warming. Students collected and analyzed data and shared it with the other 80 sites in 30 countries. It has grown to 3,500 schools in 61 countries. In 1991, students ranging in age from 11-18 years, from six continents, measured air and soil temperatures, precipitation, bird and insect presence and the stage of plant growth thus linking meteorological, physical and biology observations to a major seasonal event and creating a "snapshot" of the planet.[27] Buckminster Fuller had earlier named such a global device, created to describe a holistic phenomena, as a "macroscope."

The significance of these projects are numerous. These networks are examples of project-oriented science education. It permits all students, regardless of their grade level or academic preparation, to participate in experiments that pertain to real scientific problems of social significance. Students create maps displaying a holistic phenomenon drawn from a mosaic of local measurements. It engages scientists and encourages them to communicate with students and direct them to information sources not readily available in the classroom. It permits teachers to act as consultants rather than lecturers. Thus, computers and telecommunications create a global classroom and a new alternative infrastructure for education with a social significance.


The economy, science, technology and education are highly interrelated. Science-driven innovations create new industries and new jobs. Technology increases productivity but requires a more highly skilled work force with a broader education and a greater familiarity with the tools and theories of science. Competitiveness depends not only on the discovery of new innovations, but the speed at which that knowledge is transmitted through our educational systems to create highly skilled workers who can apply their knowledge.

The information explosion has greatly increased our understanding of the world about us. However, the growth and exploitation of information rests not only upon the ability of scientists to produce new knowledge, but also upon societyís capacity to absorb and use it. Therefore the real limits of scientific growth may well be manís limited ability to absorb new information.

Modern science and mathematics are not widely taught in our schools and colleges because it is believed to be too difficult, too expensive or too esoteric. But if we are to be internationally competitive, we must find a way to provide all our students with access to the new intellectual tools of modern science. Global networks can deliver these tools and resources to every classroom, workplace or home.

Research shows that educational technology, when properly applied, can provide an effective means for learning. However, the new intellectual technologies offer new and better ways to expand human capacity, multiply human reasoning, and compensate for human limitations. Powerful technologies are now available to significantly augment the skills that are necessary to convert data into information and transform information into knowledge.

The world of education has changed from an orderly world of disciplines and courses to an infosphere in which communication technologies are increasingly important. While education is changing, it is not changing fast enough. It is clear that in the future we will see a major restructuring of our social, industrial and educational institutions, and an increased reliance on computers and telecommunications for work and education.

However, the driving question for education in the 21st century will be that posed by Herbert Simon on what it means "to know." Is it what we have in our heads or how well we are skilled to explore the infosphere?


Andrew Molnar is a 21-year member of T.H.E.ís Editorial Board. He received his doctorate in Psychology at the University of Maryland. From 1966 to 1970 Molnar directed programs in higher education research and coordinated the educational technology programs at the U.S. Department of Education. From 1970 to 1995, he was the Program Director for Applications of Advanced Technologies Program in science education at the National Science Foundation.


  1. Bell, Daniel, "Communications Technology for Better or for Worse," Harvard Business Review, May-June (1979), pp.20-28.
  2. Bernier, C. L., "Reading Overload and Cogency," Information Processing and Management, 14, (1978), pp. 445-452.
  3. Simon, Herbert A, "Designing Organizations for an Information -Rich World," in Martin Greenberger (Ed.) Computers, Communications, and The Public Interest, Baltimore, MD: Johns Hopkins Press, (1971).
  4. Reinick, Lauren B., "Mathematics and Science and Learning: A New Conception," Science, April 29, 1983.
  5. Gagne, Robert M., "Is Educational Technology in Phase?," Educational Technology, February, 1980, pp. 7-14.
  6. Levien, Roger E., The Emerging Technology: Instructional Uses of the Computer in Higher Education, New York, NY: McGraw-Hill Book Company, (1972).
  7. Informational Technology and its Impact on American Education, Office of Technology Assessment, U.S. Congress, Washington, DC, (1982), pp.128-133.
  8. Molnar, Andrew R., "Computers in Education: A Historical Perspective of the Unfinished Task," T.H.E. Journal, 18(4), November 1990, pp. 80-83.
  9. Kemeny, John C and Thomas Kurtz, "Dartmouth Time Sharing," Science, Vol. 162, October 11, 1968, pp. 223-228.
  10. "Patrick Suppes" in Robert T. Taylor, (Ed.) The Computer in the School: Tutor, Tool, Tutee, New York, NY: Teachers College Press, (1980), pp. 213-260.
  11. Papert, Seymour, Mindstorms: Children, Computers and Powerful Ideas, New York, NY: Basic Books, Inc., (1980).
  12. Harel, Idit and Seymour Papert (Eds.) Constructionism, Norwood, NJ: Ablex Publishing Corp. (1991).
  13. Molnar, Andrew R., "Viable Goals for New Educational Technology Efforts: Science Education and the New Technological Revolution," Educational Technology, 15(9), September 1975.
  14. Kulik, James, and C. Kulik, "Effectiveness of Computer-based Instruction: An Updated Analysis," Computers in Human Behavior, 7(1-2), 75-04, (1991).
  15. Brown, John Seely, "Uses of Artificial Intelligence and Advanced Computer Technology in Education," in (Eds.) Robert J. Seidel and Martin Rubin, Computers and Communication: Implications for Education, New York, NY: Academic Press Inc., (1977).
  16. Anderson, John R., Rules of the Mind, Hillsdale, NJ: Lawrence Erlbaum Associates, (1993).
  17. Feurzeig, Wallace and John Richards, "Intelligent Tools for Algebra," Technology and Learning, 2(3), May/June 1988.
  18. DiSessa, Andrea, "Artificial Worlds and Real Experiences," Instructional Science, 14 (1986) pp. 207-227.
  19. Stanley, Eugene, "Learning Science Through Guided Discovery," Science and Mathematics Education Center, Boston University, Boston, MA ([email protected]).
  20. McLuhan, Marshall, "Our Dawning Electric Age," in Emmanuel G. Mesthene (Ed.) Technology and Social Change, Indianapolis, IN: Bobbs-Merrill Co. Inc., (1967).
  21. Clarkson, Mark A., "An Easier Interface," BYTE, February, 1991.
  22. Means, Barbara, "Enhancing Skills Through Distant Mentoring," SRI International, Menlo Park, CA ([email protected]).
  23. Fisher, Gerhard, "Mastering High-Functionality Systems by Supporting Learning on Demand," Department of Computer Science, University of Colorado, Boulder, CO, ([email protected]).
  24. Jennings, Dennis, et. al., "Computing, Networking for Scientists," Science, February 28, 1986.
  25. Grand Challenges: High Performance Computing and Communications. FY 1992, Office of Science and Technology Policy, Washington, DC, (
  26. HANDS ON!, Fall Issue 1991, TERC, 2067 Massachusetts Ave., Cambridge, MA 02140; also see: Candace Julyan and Stone Wiske, National Geographic Kids Network (
  27. 27. Science Education News, April, 1991; also see (

This article originally appeared in the 06/01/1997 issue of THE Journal.