In this fourth generation of thinking tools, the digital age, many trends are apparent. Twenty-first century awareness of these digital trends and the thinking on which they are based is essential to effectively predict and plan for educational needs. This includes the trends of: increased capacity while decreasing size and cost; increasing ability in using and building computer communication systems; fast and increasing pace of change; increasing rates of social interaction; and the increasingly significant role of unpredictability. These factors in turn drive discussion about human capacity to change, to learn and grow. In short, change dominates world culture.
What has become known as Moore's Law has held true since the 1950's. Moore, co-founder of the chip manufacturer Intel, observed that the number of transisters that can be placed on an integrated circuit chip was doubling every two years. In fact, the interval has varied from 18 months to 2.5 years. As good as chip design improvements have become, the increase in the capacity of other digital devices has come even faster. Figures would indicate that the doubling in mass storage capacity of hard drives is occurring almost every 12 months.
Moore's Law (webopedia, wikipedia) provides a deeper sense of how fast change and
innovation is occurring in the technology of computers. One impact
of this is to cut the cost of the computer components in half about every
two years, which
has
an impact on a wide range of computer technologies. Further, as chip components
shrink, the distance electrons have to travel decreases making the chip
also faster. Krdyer's Law predicts the same trends in hard drive storage space (Walter, 2006). Hendy's Law shows the same trend with pixels per dollar in digital cameras (see clickable graph on left). This prediction has held true for over fifty years. Our economy is increasingly based on this prediction remaining
true.
Many have noted that chip technology is approaching the limits of physics. There is a finite point at which one cannot further shrink the size of transistors on a chip and still have them function. Unless nanotechnology or some other technology actually becomes operational, this trend will end according to some predictions about the year 2015. If true, this could have a wrenching negative effect on world economy. In the meantime, the size and cost of computers will continue to drop. Kurzweil (2001) researched the longer view of information storage. He found that this information doubling can be traced to the late 1800's, that over this time four different technologies took turns at leading this effort then became obsolete with chip technology now being the fifth. He concluded that this trend of new technologies picking up the baton when the last technology leader fades will continue for decades.
Whatever the size or speed of computer, all of the control and management of the computer is now done through "chips" or transistors of various materials and not all of them are made of silicon. The chips that are the CPU (central processing unit) and the memory chips are made of switches, assembled from very tiny transistors. Millions and billions of these transistors can be placed on fingernail size chips. These transistor switches are also known as bits which can be thought of as switches that are on or off. All computer directions and commands depend on the condition of a collection of these switches. Mathematically the switch is either a 1 or a 0. The mathematical concepts of base two or binary language and Boolean logic are used to control these switches through what is called low level language or machine language.
As the size of the switch is decreased, computer designers can pack more computer directions into the same space, making it more powerful yet use less electricity. Intel announced on January 25, 2006 that it had produced a RAM (memory) chip with 45 nanometer logic design, reporting that the "45nm SRAM chip has more than 1 billion transistors" (TechTree, 2006). In higher level programming languages such as Logo, Pascal, Fortran or C++, one programming term sets the status of a large number of these bits (switches). Rows and rows of these programming commands are used to create large collections of computer code referred to as programs or applications. If the programming code that was needed for your average word processor of today was printed out for display, it would consist of many millions of lines of code. These chips work with mechanical devices to handle the input and output of bits that the computer uses to direct its work. These mechanical devices include modems, printers and disk drives.
Because of Moore's Law, computers have gone from requiring the space of a small building, to the size of a room, to the size of an item to fitting on a desk, to a lap and then to hand-held devices sometimes called PDAs or Personal Digital Assistants. Many institutions have explored such developments. For example, Western Carolina University began research in this area (2000-2001) to explore the impact of "palmtops" on student learning and community development. A special genre of PDA computers is referred to as "pen computing" which means using a pen-like device for computer input instead of a keyboard. Though much of the current use of computers is focused on desktop and laptop systems, the cutting edge of personal computing is the merger of PDAs and cell phones to create Smart Phones such as the iPhone.
The overall trend is towards the ever smaller from which we can predict even more tiny personal computing devices in the years ahead. Given the limited workspace of students and their high degree of mobility, the ongoing development in hand-held and smaller devices needs to be closely monitored by educators.
Optional readings:
A variety of computer technologies are already wearable. Chips,
sensors and processors can now be woven into clothing. Entire web sites
are devoted to news of such developments. The time will come in the next ten years
when the "form factor" or size and shape of the computer will be so small
that where we wear the computer will become a major design issue. Will
we want our voice activated computer to dangle from our ears, slide behind
a belt buckle, or perhaps be implanted underneath our skin like heart pacemakers
or reside in the surface of our skin like a tattoo. Such tiny computers
would have to come with warnings about keeping them out of the reach of
small children who might swallow them.
Based on the research into the design of nanocomputers, this trend towards downsizing the computer is far from over. Nanotubes, made of carbon atoms, can be arranged in three dimensions, be 100 times as strong as steel, and be used for computer functioning. As an example of future designs, just one of the red blood cells in your body can be thought of as a tiny machine. The evolution of nanocomputer technology will enable us to build even better red blood cells or other microscopic machines. In the future the doctor may prescribe that we swallow thousands of nanocomputers floating invisibly in a spoonful of liquid in order to complete diagnostic lab work or to clean out a clogged artery. The prefix "nano-" in this context is referring to a billionth of a meter, an area 3-4 atoms wide. When nanotechnology is fully developed, machines will be assembled and snapped together using the chemistry of atoms and molecules. Their assembly factory will be a test tube. To program a computer may come to mean the same thing as making a kind of computer, erasing the distinction between hardware and software. Such computing design has been called 3-D molecular computing by Ray Kurzweil (2003). Others refer to this as DNA computing. A DNA device designed to play tic-tac-toe and called MAYA has not been beaten (New Scientist, August, 2003).
This nanotechnology is a spin-off of early thinking on bio-chip technology.
Early experimental work on biochips in the 1980's had a theoretical limit
of 100,000 gigabytes within a cubic centimeter of material. In one set
of calculations that I developed, this cubic centimeter could store the
equivalent 5 hours of television or video per day, 365 days per year, for
3.2 years. With 5 of such biochips you then could record audio and video
of every waking and sleeping minute of your life for those 3 years. As
an approach to innovative thinking, have your students cut out a cubic
centimeter of clay, weigh it, reshape it to fit into or onto different
places and make some inferences about the possibilities, such as the future
portability and availability of information. Current technology will far exceed the theoretical limit set decades ago. Nanotechnology study could be combined with the theories and study
of quantum mechanics and emergent biology to create a world that is perhaps
beyond our current understanding and at the very edge of our imagination
today.
Optional readings:
The great, great, great, great, great, great, great grandfather computer chips of today's leading computer chips still outsell in quantity the cutting edge computer chips that are sold by the billion. That is, the 8080 microchip which was first built in 1973 and other older computer chips, sometimes called microcontrollers, outsell today's microprocessor chips by a factor of four, selling over five billion in 2002. What could this high and increasing demand for "ancient" chips mean?
For a number of reasons, as these older microchip designs fell from their original and expensive cutting edge roles, they became easy and cheap to manufacture, just fifty cents or less for an 8088 microchip today. Today's cars, refrigerators, elevators, security systems and most everything electrical now include them in subcomponents by the dozens. As the price for microchips plummeted, their management and control functions were so useful that the chips were increasingly considered for integration into almost everything. "What's more, we haven't even spun out all of the potential applications of the 8080, Z80, or 6502, much less the 80386, Dec Alpha, or the first PowerPC" (Malone, 2003). In short, the integration of computer technology into almost every aspect of our agricultural, industrial and cyberspace tools is doubling in tempo with the beat of Moore's Law. This in turn means that our cultural systems rapidly incorporate computer technology into our thinking, philosophy and psychology, just as happened with the introduction of writing into human culture.
The cost of making the next generation microprocessor also continues to double. Eventually, this effective recycling of old designs will create a "recycled" economy that competes with and rivals the cutting edge designs, an event that could slow the pace of chip design and the pace of change within the computer industry. For example, it may be more effective in cost and speed to put a large collection of 30386 chips together to make a personal computer than to buy one high cost cutting edge chip. At some point the doubling cost of research and development will not be sustainable. This will not necessarily slow down the overall pace of change, as other developments, particularly in biology, may next drive the high rate of change. A slowdown in chip design, however, is not yet visible nor predicted for many years.
Optional reading:
Toffler observed (1970) that cultural change in the information age was occurring faster. More importantly he noted an important impact of such change. Sufficient rapid change produces cultural and personal shock, a summation that I will refer to as Toffler's Law. On the one hand, change leads to many opportunities for improvement. On the other hand, for those insufficiently prepared, encounters with a high rate of change can lead to a kind of mental shock, a kind of paralysis in ones ability to think and respond to the change.
A simple observation of such improvement is the computer's gradually increasing capacity to handle multiple media that as net speed increases is increasingly distributed over ever larger regions of the Internet. Composers can increasingly work with an ever bigger palette of options including text, still images, audio, animation and video, yet among an audience with the greatest capacity to benefit from such difference, the print based nature university "textbooks" have changed little over the last several hundred years. Toffler's law argues that the shock of change causes the intended impact of a technology to appear much later than generally expected. For example, for years after corporations had widely adopted hourly computer use by their employees, statisticians failed to find any positive economic impact of those computers. Today information technology is a central factor in economic growth.
More importantly, the rate of change is accelerating. "At today's rate of change, we will achieve an amount of progress equivalent to that of the whole 20th century in 14 years, then as the acceleration continues, in 7 years. The progress in the 21st century will be about 1,000 times greater than that in the 20th century, which was no slouch in terms of change" (Kurzweil, 2003). It takes time to adapt. School innovators incorporating information technology will require some patience as computer skills develop. But developing an educational system that thrives on such change and eliminates the problem of "change shock" becomes an increasingly important priority. Should you question whether thriving on change is possible, spend some time with a three year old.
Optional reading:
Partner to today's computer is the network. Said another way, the network grid is the newest computer. Home and business offices have moved from telephone based dial-up modems to many times faster broadband speeds that are delivered by a variety of telecommunications companies including cable TV, telephone and satellite companies. These telecommunication companies draw their bandwidth from even faster trunk lines. Networks began offering commercial rates for 10 gigabit (GB) ethernet connections using optical fiber lines in May of 2001. The good news is that there will be plenty of capacity for future growth. Bandwidth Scaling Law shows that network bandwidth is increasing at close to the same rate of doubling as the capacity of computer chips. Butter's Law of Photonics states that the quantity of data at the same price coming from an optical fiber is doubling every nine months. Best estimates into the foreseeable future are that this network trend will continue. Networks with speeds of 100 gigabits and higher are being planned for Internet2 system.
Computers and their networks will continue to become much faster, cheaper and more powerful. The original approach to computer speed was to produce a single chip, the CPU, that could process data faster than the last design. The world's very fastest computers were referred to as supercomputers. A "high-water" mark of one billion floating-point operations per second was first used to help define the meaning of super-computer. That is of course a very old and outdated standard for supercomputers today. Personal computers sold at standard PC prices have already entered the market place that are reaching those speeds and beyond. With the advent of fast computer networking, another approach to faster processing of a task or problem has emerged, the team approach. A computer can assign different parts of the task to a team of different CPUs in the same computer or to different computers across a network to integrate the results at a particular computer. This is known as parallel and grid computing and using a very large number of computers for this work is referred to as massively parallel computing. Several projects are now underway which work on problems that use the idle time on thousands of computers scattered across the Internet. For further information on this topic, search the Internet's information databases for this phrase. It is also useful to read and participate in the newsgroup, comp.parallel, which discusses hardware and software developments for massively parallel computing.
Optional readings: massively parallel computing ; comp.parallel newsgroup ; for more information search using these terms: supercomputer; massively parallel computing.
Future revolutions in size and speed are likely to come from biocomputing, optical computing and quantum mechanics. Our computers are currently totally dependent on electrons to move bits of data around within the computer. Electrical storage of data may give way to the use of photons instead. That is, instead of electrical transistors, photo transistors would be placed in our "chips" as part of RAM and ROM. Instead of electrical wires, we will increasingly use fiber wires for internal connections just as telephone companies are rapidly replacing their telephone trunk lines made of copper with glass fiber that transmits light waves instead of electrons. This will have a dramatic impact on the speed, capacity and size of the computers and their networks. For example, Motorola announced in September 2001 that it had solved a 30 year optical silicon chip puzzle on how to grow light-emitting semiconductors on a silicon wafer (chip material). This discovery integrates electron designs and photon designs through an intermediary chip layer that bonds well with both silicon and light emitters. Such work promises a ten-fold speed increase in computers when these designs reach the marketplace in the years ahead. In terms of speed, current conjecture is that computers will be hundreds of times faster than today's computers.
Perhaps as optical computer networks and systems advance, we will think
of electron based information storage and transmission the way we think
of the steam-age of motor cars, an interesting era that happened long ago.
Educators in the public schools, which are generally hard pressed to have
any or at least adequate computing and network resources, must strain a
bit to imagine the world that is soon coming, a world where the vast majority
of its citizens of any age have all the computing and communication power
they can use, as often as it is needed.
Metcalfe's Law and Reed's Law predict the increasing value of computer networks as the number of network participants and networked devices and the number of network groups increase. Metcalfe's Law says the "total value of a communications network grows with the square of the number of devices or people it connects (N2)." Reed's Law observes that the number and value of group-forming options grow exponentially as the N or number of people in a network increases (2N). Further, Reed's Law would indicate that the value of group forming options grows even faster than the growing value of the network itself. (See spreadsheet comparison.) Moore's Law and the Bandwidth Scaling Law say that the difficulty of acquiring computer technology and getting adequate computer network bandwidth will continue to drop. That is, our ability to build both local and global problem solving networks will continue to become easier.
It is intriguing that at the same time these trends were beginning to shoot upward, educational researchers were advancing the concepts and methods of cooperative learning. Led by David and Roger Johnson's research of the 1980's long before the concepts of the Internet and the World Wide Web were commonplace, a new perspective was emerging on organizing learners for educational effectiveness. The concepts of cooperative group behavior (Johnson et al, 1985a, 1985b; Johnson, D. W. and Johnson, F. P., 2003) merge well with these previously discussed long term trends. Educators that re-examine the cooperative ideas that grew from Johnson and Johnson's study and look for ways to apply those group and collaboration skills using the emerging communication tools of an increasingly networked world, will develop new models that will not only revitalize education but lead community and economic development as well.
The good news for educators is that real skills with using the Internet and the formation of groups using computer networks will contribute to significantly improving two of the most important factors in learning, the rate of interaction and the depth of interaction. Educational interaction includes many factors, including students, curriculum, parents, teachers, administrators and more. The bad news for educators is that K-12 students seldom are taught network interaction applications let alone use computer networking tools in school (e.g., email, Instant Messenger chat, newsgroups, peer-to-peer networking and so forth). Instead this activity goes on in homes of those connected to the Internet without educational guidance or improvement. If anything, this knowledge, which is critical to current economic growth, is too often seen as frivolous or dangerous for school productivity. At the same time, the need for this interaction knowledge is increasing exponentially.
Taken together, the network lever of bandwidth scaling law along with Metcalf's law and Reed's law encourage and improve socialization and collaboration, both of great value to educators. Vygotsky's work and the constructivist movement in education have long recognized the social nature of learning. Further, the global nature of the Internet has allowed teams to form across vast distances of geography and time. Collaboration is also increasingly recognized as a critical aspect of creativity and innovation (Paulus & Nijstad, 2003; Sawyer, 2007). The fruits of such change are readily visible. Friedman's (2005) hypothesis that the "world is flat" comes from his research on the resulting radical change in the nature of economic competition and collaboration on a global scale from 1989 to the present. The creative nature of digital collaboration is one more lever that accelerates change.
Inferences use historical data to predict. One of the most novel hypotheses based on computing technology trends was done by the mathematician Vernor Vinge. His inference thinking (1993) led to his prediction of a "technological singularity", a technological change so rapid and profound that it represented a transformation that breaks with known historical patterns, something comparable to a caterpillar which transforms into a butterfly yet occurs to human life itself. This thinking about a forthcoming transformation of human biology and intelligence in the next few decades was further researched and extended by Ray Kurzweil, forming the Law of Accelerating Returns. 
The above formula that was derived from this research "is a double exponential--an exponential curve in which the rate of exponential growth is growing at a different exponential rate" (Kurzweil, 2001). Further, the data from both thinkers would suggest that this event will occur in the lifetime of many who are now reading this sentence. Click the thumbnail picture on the right for the full-sized chart of some of the data points in just one of his charts.
Inferences are just predictions, but the better ones are hunches based on known data. The concept of a technological singularity transforming humanity has become the basis for much discussion and debate over the degree of possibility and the value of such an event. Those interested in further evaluating Kurzweil's numerous data points, trend charts and heavily referenced thinking should read his 58 page essay, the Law of Accelerating Returns. Information technology is just one of the emerging technologies that are used to support the idea of transhumanism. The World Transhumanism Association defines it in part in this way: "The study of the ramifications, promises, and potential dangers of technologies that will enable us to overcome fundamental human limitations, and the related study of the ethical matters involved in developing and using such technologies" (WTA faq, 2006).
Computer technology has been the underlying technology that has enabled and/or accelerated a set of other emerging technologies. This collection of emerging technologies are referenced by a series of acrononyms that one may encounter in current readings. These include: GRIN (Genetic, Robotic, Information, and Nanotechnology); GRAIN (Genetics, Robotics, Artificial Intelligence, Nanotechnology); BANG (Bits, Atoms, Neurons, Genes); GNR (Genetics, Nanotechnology and Robotics); and NBIC (nanotechnology, biotechnology, information technology and cognitive science).
Are there fundamental concepts that would undermine the "always progressing" technological determinism implied by the thinking of Vinge and Kurzweil and many supporters of transhumanism? There are.
Unpredictability emerges in different ways in different aspects of our use of computers, in debugging them and in depending on their inference potential. There is deep irony here. The conceptual framework for the design of computers began with the need of a more reliable mathematics machine then expanded to the goal of long range prediction. One of the motivations behind the first mechanical computer was the need to eliminate human error in mathematical calculations. A central motivation for early computer design was long range weather prediction. The former problem of error encountered the problem of complexity and the latter problem of predictability encountered a transformation of understanding. Both are fundamental problems.
These fundamental unsolved problems have quite a history. Long before the invention of computers, errors in basic addition and subtraction found their way into tables used in ship navigation and finance which caused further errors in larger social systems. There was also great need to speed the calculation process. Computers have handily addressed these problems of several centuries vintage. However, as computer programs grew in size and complexity and became connected to computer networks, it became impossible to totally debug them. The deepest of ironies however was related to fundamental mathematic and scientific understanding. Computers were essential to discovering nonlinear mathematics. This math finally unraveled the hope for computing to produce reliable long term prediction in subjects that mattered most. Predicting the weather months or years in advance is a classic example.
Software Design: Bugs and Complexity
Faster and cheaper computers do not necessarily mean smarter and better performing computers. Computers are dependent on software. As valuable as computers have become, our inability to thoroughly debug them has become a major source of computing unpredictability (Cooper, 1995; Cooper, 1999; Festa, 2001; Kaner & Pels, 1998; Levinson, 2001; Mann, 2002). Wirth's Law that "software decelerates fasters than hardware accelerates" points to the problem of scale as programs become larger and thereby more complex.
Computers can be just as unpredictable during insignificant moments as during our most significant need for them. Failure can occur in more subtle ways than a hardware crash. Sometimes the failure is one of insufficient design thinking. Computer software too often include features that are hard to use and confusing, which causes human mistakes. Further, the computer software code in too many programs has become so large and complex that the average computer application program and its interaction with the operating system cannot be thoroughly debugged and never will be. Also, the complexity of the code running computer networks and their openness to so many users creates additional vulnerabilities from viruses and other intentionally misbehaving code which can bring down or hobble a computer and its network. Designers must work from these observations and build systems that expect imperfection and do a better job of handling it.
Newer operating systems and applications should be getting better at this over time. However, designers have some distance to go in making computers match the reliability of other common devices such as a toaster or a TV. The release of the Windows XP operating system is a good example of current practice. "Microsoft released Windows XP on Oct. 25, 2001. That same day, in what may be a record, the company posted 18 megabytes of patches on its Web site: bug fixes, compatibility updates and enhancements. Two patches fixed important security holes. Or rather, one of them did; the other patch didn't work" (p. 34, Mann, 2002). Such problems are common across the software industry. In a multiyear study of 13,000 programs by Humphrey of Carnegie Mellon, the research showed that "on average, professional coders make 100 to 150 errors in every thousand lines of code they write" (Mann, 2002). Finally, there is a certain economic incentive in releasing software programs before they are fully tested. This allows the software company to appear to stay ahead of or up with competitors and lets consumers debug products through their complaints, while the same consumers pay maintenance fees and "incident support" for resolving problems with what they bought. Many software engineers say that software quality is not improving. Instead, they say, it is getting worse.
Software failures due to familiar, readily prevented coding errors, have cost companies and governments tens of billions of dollars a year (Levinson, 2001). When computers become part of other machines, from X-ray equipment to weapons, software failures in these machines have also led to deaths. Some programmers have concluded that until software firms start facing and losing significant product liability lawsuits, software will not improve. So far, software firms have used their software license as a shield against lawsuit. It is doubtful that such a practice will be able to continue much longer.
Even if programmers did not make basic mistakes in their code, there is still the problem of programs becoming so large and complex that no single person can examine and understand all of the possible problems of interaction that may occur between different interacting elements of the program and points of interaction with input from outside the program. In short, our computers and the software applications that run on them do not and cannot live up to the ideal of the flawless computer operations running flawless logic. Since both can be easily flawed, computers fail or crash far too frequently and at times in which that failure can have a major impact. That is perhaps the most concise summary of the state of art in computer design.
Optional readings:
Charles C. Mann (August, 2002). Why Software Is So Bad. Technology Review: MIT's Magazine of Innovation. pp.33-38.
Cooper, Alan (1999). The Inmates are running the Asylum: Why High-tech Products Drive Us Crazy and How to Restore the Sanity, Cooper Books.
Cooper, Alan (1995). About Face: The Essentials of User Interface Design. Cooper Books. [read free online chapter]
Kaner, Cem; Pels, David L. (1998). Bad Software: What To Do When Software Fails.
Festa, Paul (November 28, 2001). The root of the problem: Bad software. CNET News.com [http://news.com.com/2008-1082-276316.html?legacy=cnet]
Levinson, Meredith (Oct. 15, 2001). Let's Stop Wasting 78 Billion a Year. CIO.com. [http://www.cio.com/archive/101501/wasting_content.html]
For more readings, search for: bad software.
May's Law
Software failure however, in spite its importance, pales in significance in comparison with the more fundamental forces of unpredictability. The goal of long term predictability had been kept alive since the mathematical work of Isaac Newton in the 1600's. Newton's belief was in turn spurred on by Laplace (1749-1827), a brilliant French mathematician, and Von Neumann, an early designer of the architecture of the modern computer in the 1950's. May's Law (or May's equation RX(1-X)) is perhaps the simplest example of the many nonlinear models that put severe limits on such scientific pursuit, even though the perception of long term predictability lingers in social and political circles. It should also be noted from this basic equation that nonlinearity's uniqueness does not come from complexity but from the nature of its simplest interactions. At a certain threshhold of growth, tiny changes can build on each other to create radical and unpredictable change. May's simple mathematical expression is but one example which demonstrates that key chemical, biological, physical and social systems important to science and our culture diverge unpredictably and exponentially with time. This phenomena plays out repeatedly on the stock market in ways that continue to defy understanding (Mandelbrot & Hudson, 2004) in spite of copious amounts of data and deep knowledge of past patterns. Better and faster computers will not and cannot change this.
Without computer technology, researchers had been unable to see that the impact of nonlinearity is everywhere nor to be able to describe its nature, a turbulence that defies long term predictability yet produces phenomena which can be predictable over the short term. Weather is a classic example with which all are familiar. Predicting an afternoon without rain weeks ahead for a picnic day cannot be done, but once a thunderstorm emerges, its track and duration can be be predicted with some reliability. Such self-forming phenomena emerging from nonlinear and unpredictable backgrounds are called solitons in physics and in other places referred to a singularities. Nonlinear science would indicate that the emergence of Kurzweil's transforming singularity in human culture is not impossible, but less likely than the Law of Accelerating Returns might imply. The unpredictability of the dominant form of system behavior, nonlinear systems, makes it impossible to predict just what the future will hold. Bugs in computer systems compound the problem but even computing perfection cannot solve it.
There is a much deeper discussion of the philosophy of change (Mortensen, 2006) which recognizes that the nature of change is still not completely understood. As we teach to create change in people's lives, examining the nature of change requires thinking beyond the digital trends. How intriguing that our species continues to make computer tools for thinking and keeping up with change, tools that are imperfect, yet functional and of such importance to new knowledge and understanding that great effort is still spent in using and improving them. Yet software and hardware updates to such technology do not arrive every hour. Updates are spread out over many months. In general, there is much about one day or one year that is just like the last. 
This poses two major problems for the change challenge. How much change is really happening? 
Further, how difficult or easy is it to handle even simple changes?
How great and how uniform is the change challenge? Does one of these pie chart graphs best represent the current cultural situation across the range of human cultures and settings? If not, an Excel file with a graph of these change percentages is available to experiment with different views of the nature of change; just change the numerical data and the graph automatically adjusts. Measuring the change challenge from one individual or one country to another must involve significant variation. What will the degree of change look like in the years ahead for you? How well does and should our educational system prepare us for what Piaget (1969) referred to as accommodation, accommodation that is the result of both rapid change and wrenching unpredictable adjustments? Coming to terms with some measure of how much change needs to be addressed is an important step in planning curriculum for the future.
The challenge is to use our thinking tools to understand and to teach how to thrive on increasing change, diversity and unpredictability. It can be done. After all, every living organism has mastered this to some degree. Yet adaption to change and the need for change have proven difficult for human beings. Changing human behavior to confront even known and directly personal problems is difficult. One assumption is that failure to change is due to a lack of information, that change in behavior happens because a good analysis of the problem has occurred and that those involved have accurate factual information that they understand. In fact, even if adequately informed and the analysis is clear, only 1 in 10 people dealing with severe life-threatening illness will end the habitual behavior that caused the problem in the first place (Deutschman, 2005). Smokers keep smoking; over-eaters keep eating; heavy drinkers keep drinking. Threat, even severe threat, is insufficient.
Three elements have proved effective in building the capacity to change (Deutschman, 2005). People need a deep emotional positive frame of reference to sustain efforts to change. Joy is more effective than threat. Big changes that create major improvements quickly are more sustainable than incremental changes over a longer periods of time. Sustaining such change requires ongoing support until new habits become established.
As a willingness to learn is a willingness to accept change, people need to continue to learn something new to stay adaptive. This maintains a habit of change. It is the author's observation and that of others (Devault, 1965) that the desire to learn carries through to teaching itself. Those that maintain their capacity for great teaching are those that are still learning. An educational system exists to invent, foster and sustain change.
The impact of these trends has been as deep economically as it has been psychologically and intellectually. Since, 1900, educational expenditures have formed the most rapid paced exponential curve of them all (Kurzweil, 2001). "Thus, for the past two centuries, automation has been eliminating jobs at the bottom of the skill ladder while creating new (and better paying) jobs at the top of the skill ladder. So the ladder has been moving up, and thus we have been exponentially increasing investments in education at all levels" Kurzweil, 2001). This upward movement to better jobs and better lives is part of the positive frame of reference that sustains efforts to change. The educator's challenge is to design and teach the curriculum that enables learners to deal effectively with the forces of change.
Given these trends, change will remain one of the major ongoing problems of the 21st century. Yet, people have learned to live with the unpredictability of weather. Humans are capable of even greater challenges than that. Given this capability for adaptation, there is a silver lining for educators to all these trends. This cloud of increasing change certainly expands the market and need for teachers and education. Teachers and education are both the positive potential of all these lines of inference, from Moore's Law, to the Law of Accelerating Returns to May's Law, and also the antidote to their negative implications.
Original version, 1996; Updated Version 15.8 | May 10, 2008 | Bibliography | History Index | Chapter 1 | Author: Houghton
