On the Value of Qualifications - CiteSeerX

On the Value of Qualifications: Why go to University or Technikon? Philip Machanick Department of Computer Science University of the Witwatersrand 2050 Wits, South Africa [email protected]

http://www.cs.wits.ac.za/~philip Abstract In today’s world of work, qualifications are important. Just knowing how to do something is not enough, because job descriptions are constantly changing. Understanding what is behind what you know gives you an extra edge, because you can adapt faster to change. This paper explains some of the forces behind the rapid changes in computer technology and how education, as opposed to training, adds to your ability to cope with change.

1 Introduction In recent times, there has been much confusion over the value of qualifications. Some short-term trade “qualifications” have been oversold, with the result that the misconception has been spread that there is no longer a skills shortage in the computing world. On the contrary, there remains a serious skills shortage, but a shortage of the kind of skills that cannot come from a course lasting only a few weeks. This paper presents the case for qualifications with a longer-term focus than the typical short course sold through private training institutions. It does not suggest that such short-term courses have no value, but rather, that their value has to be seen against the fact that all knowledge is under risk of becoming obsolete rapidly in a fast-changing field. First, some background is presented. The rate of change in computing is an underlying cause of the need for longer-term qualifications, so some detail of changes in technology is presented. Next, the range of options is outlined, including the difference between education and training. Finally, the concept of professionalism is explained. In conclusion, the case for education as opposed to training is summed up.

2 Background: How the Industry Changes The computer industry is changing rapidly. There are two major kinds of change which occur. The first is incremental change, of the kind where an improvement in existing capabilities occurs steadily. That kind of change is very like inflation or compound interest, but in a positive sense. Because a fixed percentage improvement occurs every year, the overall effect is exponential growth. The second kind of change is a paradigm shift, in which there is a fundamental shift in our model of work, how we think of technology, and how new technologies are produced. A paradigm is just a fancy name for a model. Paradigm shifts, in practice, are harder to achieve than incremental change but when they occur, they result in a big leap ahead. Let’s consider each of these kinds of change in turn, and end up by considering what kind of change is most common—and how education helps us to deal with change.

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Figure 1. Processing versus Memory Speed Improvement the dark line is processing speed improvement; the light line memory speed improvement: plotted on a log scale, so the very big increase in speed gap is visible on this scale… it can be seen the processing speed is improving much faster than memory speed (the base is 10, so a 4 on the vertical axis means a speedup of a factor of 10 000)

2.1 Incremental Change The mathematical expression of the kind of growth resulting from incremental change is called a learning curve, and can be expressed as performance = PBt where B is the rate of “learning” of improvement over a given time interval (usually a year, the units in which elapsed time, t, is expressed), and P is a curve fit parameter [Lewis 1996]. For example, if B is 1.1, there is a 10% improvement per year. A classic example of a learning curve is Moore’s Law, named after Gordon Moore, a founder of Intel. Moore’s Law predicts the rate of improvement of processing speed of a computer. The underlying learning curve is the rate at which components on a chip can be made at a smaller size. Making things smaller results in two kinds of improvement: • parts which are closer together result in less delay in moving information, so a higher clock speed can be used • the ability to put more parts onto a chip makes it possible to do more work within one clock cycle These two improvements together result in a learning curve where performance improvement (in terms of number of instructions which can be processed in a given time) is 50% per year. In other words, every year you can buy a computer which can process instructions at 1.5 times the rate of the year before. In practice, you don’t actually see this improvement in a real computer for a number of reasons. Other components like memory and disk don’t speed up at the same rate [Hennessy and Jouppi 1991], as illus2

On the Value of Qualifications: Why go to University or Technikon?

Figure 2. A Digital Library the ACM digital library is about to be searched for one of the papers which was used as a reference in this paper

trated in Figure 1. Further, as computers become more powerful, software writers become more lazy, and less and less efficient software is produced. The first window-based version of Microsoft Word appeared on the Apple Macintosh in the mid-1980s, and could run on a machine with 128Kbytes of memory and no hard drive. Today, Microsoft Word requires a machine which is about 100 times faster, and with over 100 times the memory to run—not to mention a large hard drive. Clearly, there has been some increase in the features of the program, but no one could claim it has over 100 times more features than the first version. Finally, as we want to do more sophisticated things with computers, we demand more and more of their power. An interesting example of the scaling up of both capabilities and of expectations is the Internet. The Internet in its earliest form was a growing network connecting academic networks (an internet in general is a network of networks). Once the Internet became accessible outside the academic world, it started to grow rapidly [Leiner et al. 1997]. The speed of connections today is enormously higher than in the 1960s and 1970s, yet access can at times be frustratingly slow, because the number of users grows rapidly, and because the size of network transactions is growing rapidly. In the early 1990s, when the Internet was starting to expand outside the academic world, fetching a file of several Mbytes in size was a

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rare event. Today, people fetch software updates of that size and even hundreds of Mbytes. Worse, they expect to be able to watch movies, which require a steady rate of transfer without interruptions. Another key area of change in the use of the internet is its growing use for financial transactions [Segev et al. 1998], which require a high level of security (for example, so a credit card number can’t be stolen by someone snooping on the network). The original design was not intended to be highly secure, because it was used in an open environment: academics were mainly using the internet to exchange information about research.

2.2 Paradigm Shifts A paradigm shift is a fundamental change in the model we work with [Kuhn 1970]. An example is the way I am finding references for this article. In the distant past, I would have had to trudge to the library, search through a combination of index cards and microfiche (sheets of plastic with highly miniaturized images which can be viewed through a special reader) indexes, look for indexes in the back of journals, and ask some off-campus body to do searches for me. All of this would take up a lot of time and even money. Today, as I type the article, I can connect to digital library sites belonging to professional bodies (for more on these bodies, see Section 4; see an illustration of a digital library at work in Figure 2) and search for articles based on keywords, sections of titles and authors. I can also search the catalogue of my own university library. I can develop ideas as I type, and confirm them by finding literature. Only a few years ago, I would not have been able to write an article in this way. I would have had to spend time digging up literature, decide whether what I wanted to say was supported by the literature, if not, change the approach to my writing, and finally find more literature to back up what I was saying. In

Figure 3. Intel 4004 Processor the Intel 4004 was a 4-bit microprocessor containing 2 300 transistors and could process 60 000 instructions per second (source: [Aspray 1997])

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the case where I had to do some lengthy experiment to back up my point, I might find after doing much of the work that I had missed some major piece of published work, which either invalidated my assumptions, or which had already solved the problem I was setting out to solve. In the wider computing world, there have been major changes in the technology, which have effected the way we can work with computers. Although there were many great breakthroughs starting from early work in World War II to break German codes (not to mention pre-electronic attempts at automating computation [Kidwell and Ceruzzi 1994]), the biggest single change which effected the greatest number of people was probably the invention of the microprocessor in the 1960s [Aspray 1997]. While the first microprocessor, the Intel 4004 (which went into production in 1971), was feeble by today’s standards—see Figure 3—the shift from a computer’s processor being constructed out of many components to building the whole thing on one chip was a major technological shift, and resulted ultimately in a major shift in how people worked. A computer was no longer a big, expensive box, mainly found in large companies, but something which anyone with a reasonable salary could own. The paradigm shift to the microprocessor took a long time to convert to the personal computer revolution (which really started with the launch of the Apple II in the 1970s). However, the fact that microprocessors had a mass market (not only in computers, but in such unlikely places as washing machines and cars) meant that the microprocessor manufacturers could spend more on research and development than many of the traditional large-scale computer manufacturers. Ultimately, this was to lead to a more competitive learning curve, and force most competing forms of computer out of business.

2.3 Common Forms of Change Mostly, if you look at changes in technology, what you see is incremental improvement. Car engines today are the same basic design as 50 years ago, just better in detail. Computers today are not that much different from 5 years ago, just faster for the same money. If paradigm shifts result in such big improvements, why are they less common than incremental improvement? To make a major change often disrupts a whole industry. New methods of production must be found; details have to be sorted out. In the meantime, the learning curve for conventional technologies continues. If your great new innovation takes too long to bring to market, the “conservative” opposition has meanwhile chipped away at the lead you hoped to have. A classic example is attempts at replacing car engines by superior basic ideas. For example, the Wankel rotary engine [Faith 1975], introduced in the 1960s, had many superior properties to a piston engine—see Figure 4 for detail. It ran much smoother, and generated more power in a more compact engine. Yet it was ultimately a failure: even though Mazda was able to sell it with some success in a sports car up to the 1990s, the first company which produced the engine, the German manufacturer, NSU, went bankrupt, and it was never a success in a high-volume car. There were two major reasons for the failure: the early designs were unreliable (hence NSU’s failure to stay in business, despite producing a car way ahead of its time in most respects), and their fuel consumption was high. Once the reliability problem was solved, fuel consumption became a major issue in the mid-1970s, as a consequence of artificial fuel price increases, resulting from a joint move by oil producers to control production and prices. Because of the risks inherent in a paradigm shift, it is hard to get this kind of radical change accepted. Worse, because the change requires a change in mind-set, it is often not seen for what it is. There have been many examples of companies producing a major change, and letting the opposition capitalize on their work, because management doesn’t understand the value of their innovation. For example, when Xerox produced the first graphical user interfaces at their Palo Alto Research Centre (PARC)—based on

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Figure 4. General Design of the Wankel Engine a curved triangular rotor rotates about a central shaft, resulting in direct rotary motion, without unbalanced motions from pistons (source: http://web.ukonline.co.uk/Members/jr.marsh/wankel.html)

much innovation occurring around the United States since the 1940s [Nyce and Kahn 1991]—management failed to commercialize the concept. Although they did sell a machine using innovative ideas like windows, a mouse and networked servers, they did not try very hard to sell the concept, and did not attempt to make a cheap version that could have a mass market. Instead, Apple (Macintosh), and later Microsoft (Windows), was left to introduce the idea to the mass market. Nonetheless, paradigm shifts do occur. They are successful when an existing learning curve is running out of steam (e.g., some limit of Physics), when the advantage is so big that it overwhelms any learning curve, or when they enable something that could not be done before (e.g., the invention of the desktop laser printer in the mid-1980s made it possible for a small business to do typesetting, leading to the desktop publishing industry). So ultimately, when we need to understand what drives learning curves, and when a paradigm shift becomes possible, to be in a position to survive in a time of rapid change. If we do not understand the underlying mechanisms, we run the risk of being left behind. At best, we can only follow those who lead.

3 Education vs. Training What is it that distinguishes an ability to use what currently exists, and to adapt to rapid change? An interesting distinction to make is that between education and training [Brookshear 1985]. Education involves understanding deeper principles, why and how things work—not just what to do in a given situation. Training on the other hand is specific to a given task. How do you operate a specific typesetting machine? How do you wire a plug? Education would equip you to answer deeper questions about the same topic. What are general principles of typography? What are laws of Physics, as they apply to electricity? In the computer field, training would equip you to do specific tasks, with specific equipment, for example: • do word processing using the current version of Microsoft Word • look after a computer network with a UNIX server • look after office computers running Windows 2000 • create web pages using Microsoft Front Page

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• edit movies using an iMac For all of these tasks, specific training would be useful. You could use the tools more efficiently, and start doing useful work as soon as possible, without having to spend any time reading manuals, or trying to work out how a specific program or piece of equipment works. However, if your employer decides to upgrade something, or change to a different model of computer, you would be stuck. Further, if some big new field opened up, such as ecommerce (selling via the Internet), you would have no starting point for getting into this new field. If such big changes occurred, you would have to take another course. In fact, many of the commercial “qualifications” offered by big companies are regularly obsoleted for this very reason. As the technology they sell changes, you have to go back for refresher or update courses, otherwise your “qualification” can be withdrawn. By contrast, universities and (to a slightly lesser degree) technikons offer deeper insights into the work you will be doing. Although you may be trained less specifically in tasks in the workplace, you will be educated in the principles behind the work. As a result, as the work changes, through advances in technology or improved business practices, you will be able to adapt, without having to do new courses. A technikon generally aims to focus a bit more on specific jobs you will do once you’ve qualified than a university does. If your major interest is in being able to do a skilled job, without inventing new technology, being a leader of an industry, or managing others doing technical work, a technikon offers good options. However, if you do see yourself in a leadership role, you probably do want to go to university. If you do go to university, the background you build in your degree goes beyond technical skills but also helps you to be a skilled learner, so you can learn for yourself in future. You should also develop a much deeper understanding of what is behind technologies you work with. A degree is the best preparation for coping with paradigm shifts, because it does not tie you to specific technologies and work approachs.

4 Professionalism Would you go to a doctor who stopped reading up on the latest developments after completing medical school 25 years ago? Probably not. Yet the computer field is changing just as fast as the medical field, if not more so. Many people in the industry react to this rapid change by saying they have no time to keep up with research, but instead focus on short-term problems. As a result, they have a very short-term perspective, and fail to adapt to rapid change. Instead, they try to continue working in the same style for as long as possible, until they hit a crisis, then try to learn new technologies rapidly to catch up. What they are doing, in effect, is what a doctor would be doing if he or she failed to read the medical literature regularly. For a while there would be no problem. Then, patients would start complaining that the younger doctor down the road was curing more patients. So the older doctor would rush out and do some courses, while trying to avoid losing patients. This doesn’t seem a very efficient way of working, compared with keeping up to date on a regular basis. Today, in the computer world, despite the ongoing rapid pace of advance, it is easier than ever to keep up. Thanks to the internet, it is possible to join an international professional society, and read the best work published around the world, without waiting for a paper copy. What’s more, you don’t have to read everything in the hope of finding one or two things of interest. The big international professional societies today have digital libraries, web sites containing powerful search engines, so you can find the information you want when you want it.

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Here are some professional societies: • ACM—Association for Computing Machinery, with over 80 000 members, ACM is the world’s oldest and biggest computer professional society • IEEE—Institute of Electrical and Electronics Engineers, with 350 000 members, IEEE is one of the biggest professional societies in the world. IEEE has societies within an umbrella organization, including its own Computer Society • SAICSIT—South African Institute for Computer Scientists and Information Technologists, a South African body which publishes the South African Computer Journal, and runs an annual conference Membership of these societies can be very inexpensive. The ACM has a special price for poorer countries like South Africa, and SAICSIT membership is priced to suit the local economy. The IEEE doesn’t have a special rate for poorer countries but even so, the cost of digital library membership is not expensive considering the vast range of literature which is available.

5 Conclusion Education is an investment for the future. Education at a university is more forward-looking than education at a technikon, but both put you in a better position to cope with change than short-term courses aimed at teaching a specific technology. The excitement of being in the computer industry is that it changes so fast. The cost of this rapid change is that you cannot expect an investment in learning about specific products or job descriptions to last. If you are not interested in changing your job description often, there are many other interesting and fun options open to you. However, if you do find change exciting, then the computer industry is a great place to be. The skills shortage in the industry has enticed many people to look for short-term solutions. As a result, there is an occasional over-supply of people with very specific skills. If you take a 2-week course which is “guaranteed” to land you in a great job, remember that anyone else can do the same thing. On the other hand, if you are capable of taking on a hard problem and solving it, you have a real skill that not just anyone can pick up. If you are that kind of person, you should be thinking of getting an education—not just training which will become obsolete as soon as the industry changes. Acknowledgments I would like to thank Ian Sanders for proofreading this paper. References [Aspray 1997] W Aspray. The Intel 4004 Microprocessor: What Constituted Invention? IEEE Annals of the History of Computing, vol. 19 no. 3 July-September 1997, pp 4-15. [Brookshear 1985] JG Brookshear. The University Computer Science Curriculum: Education versus Training, Proceedings of the Sixteenth SIGCSE Technical Symposium on Computer Science Education, 1985, pp 23-30. [Faith 1975] N Faith. Wankel: The Story of the Revolutionary Rotary Engine, Allen and Unwin, London 1975. [Hennessy and Jouppi 1991] JL Hennessy and NP Jouppi. Computer Technology and Architecture: An Evolving Interaction, Computer, vol. 24 no. 9, September, 1991, pp 18-29. [Kidwell and Ceruzzi 1994] PA Kidwell and PE Ceruzzi, Landmarks in Digital Computing: A Smithsonian Pictorial History, Smithsonian Institution Press, Washington, DC, 1994. [Kuhn 1970] TS Kuhn. The Structure of Scientific Revolutions (2nd edition). Chicago University Press, Chicago, 1970. [Leiner et al. 1997] BM Leiner, VG Cerf, DD Clark, RE Kahn, L Kleinrock, DC Lynch, J Postel, LG Roberts and SS Wolff. The Past and Future History of the Internet, Communications of the ACM, vol. 40 no. 2 February 1997, pp 102-108.

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[Lewis 1996] T Lewis. The Next 10,0002 Years: Part I, Computer vol. 29 no. 4 April 1996, pp 64-70. [Nyce and Kahn 1991] JM Nyce and P Kahn (editors). From Memex to Hypertext: Vannevar Bush and the Mind's Machine, Academic Press, Boston, 1991. [Segev et al. 1998] A Segev, J Porra and M Roldan. Internet security and the case of Bank of America, Communications of the ACM, vol. 41 no. 10 October 1998, pp 81-87.

Web Sites SAICSIT: http://www.saicsit.org.za/ SAICSIT-2000 conference: ACM:

http://www.cs.wits.ac.za/~philip/SAICSIT/SAICSIT-2000/

http://www.acm.org

ACM digital library:

http://www.acm.org/dl/

IEEE Computer Society:

http://www.computer.org

IEEE Computer Society digital library:

http://www.computer.org/publications/dlib/

About the Author Philip Machanick is an associate professor in the Department of Computer Science, University of the Witwatersrand. His research interests include computer memory systems, appropriate technology for Africa and Computer Science Education. He has a BSc from University of Natal, Durban, and BSc Honours and MSc degrees from University of the Witwatersrand. His PhD is from the University of Cape Town. He is the president of SAICSIT, and of the South Africa Section, Computer Chapter of IEEE, and is chairing the Focus Group on Introductory Courses for the ACM/IEEE Curriculum 2001 standards process.

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