Seymour Papert’s legacy: children, computers, and the future of programming education

In 1984, Deng Xiaoping visited Shanghai expo and made the famous statement “computer literacy needs to begin with children” The boy in the picture went on to get a PhD in CS at the age of 23.

Once upon a time I was a happy kid living in China. And I knew nothing about computers. One day CCTV, which was the only TV network available at the time, started to broadcast a series of lectures — educational content was very common in those days, unlike now — introducing a programming language: BASIC. Suddenly, I, someone who had never even seen a computer before, wanted to learn it.

My mother was usually very supportive of my impulsive decisions. Having failed to acquire the designated textbook at local bookstores, she encouraged me to write to CCTV for a mail order. A few weeks or months passed and I forgot all about the episode, until a package came with the book! With a truly communism gesture, they even returned the money we sent it! (There might even be an encouraging letter; but I am not sure.) That moment was a highlight of my childhood memories!

My pursuit in BASIC unfortunately didn’t go anywhere. Without access to computer or any meaningful guidance I gave up very quickly on learning BASIC. Or if I persisted, I don’t remember much of it now. I do remember that in high school there were several computer lessons on Apple computers. But it was not a meaningful experience for me. I did end up taking computer science major in college later, which proves that I have always had a passion for the subject matter. But I never thought that my failure to be enlightened earlier might have anything to do with the BASIC language itself and how it was taught.

I was therefore genuinely shocked, a few years ago, reading a passage in Seymour Papert’s Mindstorms: Children, Computers, and Powerful Ideas.

BASIC is to computation what QWERTY is to typing. Many teachers have learned BASIC, many books have been written about it, many computers have been built in such a way that BASIC is “hardwired” into them. In the case of the typewriter, we noted how people invented “rationalization” to justify the status quo. In the case of BASIC, the phenomenon has gone much further, to the point where it resembles ideology formation. Complex arguments are invented to justify features of BASIC that were originally included because the primitive technology demanded them or because alternatives were not well enough known at the time the language was designed.

An example of BASIC ideology is the argument that BASIC is easy to learn because it has a very small vocabulary. The surface validity of the argument is immediately called into question if we apply it to the content of how children learn natural languages. Imagine a suggestion that we invent a special language to help children learn to speak. This language would have a small vocabulary of just fifty words, but fifty words so well chosen that all ideas could be expressed using them. Would this language be easier to learn? Perhaps the vocabulary might be easy to learn, but the use of the vocabulary to express what one wanted to say would be so contorted that only the most motivated and brilliant children would learn to say more than “hi”. (34–35)

For those who are not familiar with the QWERTY phenomenon, it refers to the layout of the typewriter keyboard which was designed to overcome a technical problem (key jams) that no longer exists, but somehow gets stuck. The keys on a QWERTY typewriter are arranged as such so that it is unlikely that two adjacent keys will be struck in rapid succession( which will create jams). Likewise, BASIC was born in an era where computers were not powerful enough to accommodate human habits. It was small (compared to other packages available at the time) so it can fit into the modest hardware of an affordable home computer; it was interactive (it was one of the few interpretive languages) so a statement can be run with instant result. Both of these factors, together with the seemingly simple vocabulary Papert mentions, contribute to BASIC’s popularity as a beginner’s programming language.

You can definitely do brilliant things using BASIC — I read somewhere there was a private joke at Microsoft that if a programming task was too difficult they would “ask Bill to do it in BASIC” — but is BASIC a good choice to introduce children to the world of computer and programming?

Luckily we didn’t get stuck in BASIC. But it seems that we are far from done learning the BASIC lesson. A recent iOS game on programming education called the Human Resource Machine revives the use of GOTO statement (a legacy from assembly language) that is notorious in BASIC and makes it look like advanced programming technique. The instance shows how easy we tend to slip into a seemingly sensible but contorted solution for the problem of computer education. It reminds us that we need to be extremely mindful about what kind of thinking we want to encourage in computer education.

Now that I have become an educator and parent myself, I think a lot more about the issue of getting to know a programming language. I think more about the goals and means of coding/robotic education. And I have come to the realization that my lesson in BASIC is nothing short of a basic lesson in coding education.

In the wake of Seymour Papert’s passing away this summer, this article is dedicated to explicating his work. I want to talk about Papert’s legacy, something that is curiously neglected if not in complete oblivion. But this is by no means a complete assessment of the man and his works. As the intro shows, I mostly want to show how his ideas resonate with mine, and how I come to understand him in the context of my own thinking through the following questions:

  • What is learning? How do we learn?
  • How should we teach programming to kids?
  • What can computer do to reform education?
  • What is computer literacy?

These questions are at the center of Seymour Papert’s lifetime pursuit. So the very act of asking these questions, I think, put his ideas in the right context. He may not be the most well informed technologist or the most eloquent writer that I know, but he is definitely the most passionate pursuer of these questions.

Papert the Man


young Papert

For those who have not heard of him, a brief summary of the man’s life is in order.

Seymour Papert was born in 1928 in Pretoria, South Africa, and went on to study at the University of the Witwatersrand in Johannesburg, South Africa, where he earned a BA in philosophy in 1949, followed by a PhD in mathematics only three years later.

Because his anti-apartheid activities, he was forbidden to travel outside South Africa. Yet he managed to leave without a passport and went to Cambridge University and earned a second PhD, also in math, in 1959.

From 1959 to 1963, Papert worked at the University of Geneva with Jean Piaget, whose work had a tremendous influence on Papert’s own.

Having met Marvin Minsky at a conference at London, Papert was invited to the U.S., joining MIT as a research associate in 1963. Four years later, he became a professor of applied mathematics, and shortly after became the co-director of the Artificial Intelligence Lab, founded by Minsky. Together, they wrote the 1969 book, “Perceptrons,” which marked a turning point in the field of artificial intelligence.

In 1967, Papert and several others (Daniel Bobrow, Wally Feurzeig, Cynthia Solomon) created Logo, the first programming language designed for children. The first implementation of Logo, called Ghost, was written in LISP on a PDP-1. A robotic version of Logo, called Logo turtle robot, was created in 1969. This is a tethered model, but perhaps the first programmable robotic system for children. From 1968 year-long Logo projects were carried out in several high schools in Lexinton, MA. The work on Logo made a huge impact on a visitor named Alan Kay, who on his flight home (according to Gary Stager) sketched a device which is known as the Dynabook, the prototype of today’s personal computer (and actually a laptop). Logo was also a primary influence on Kay’s Smalltalk, which in turn influences Scratch. In 1971, Papert coauthored with Cynthia Solomon a seminal paper “Twenty Things to Do with a Computer”. This paper marks the beginning of the modern “maker movement”.


Picture of a floor turtle from the “Twenty Things” article

Papert was the Cecil and Ida Green Professor of Education at MIT from 1974–1981. In 1985, he began a long and productive collaboration with the LEGO company, the first and largest corporate sponsors of the MIT Media Lab. I don’t know exactly how Papert contributed to the design of the LEGO Mindstorms robotics kit, but the set was named after his 1980 book and obviously inspired from his previous work in Logo turtle robot. In 1985, Papert, Minsky, Jerome Wiesner and Nicholas Negroponte became the founding faculty members of the MIT Media Lab, where Papert led the Epistemology and Learning research group. In 1989, the LEGO company endowed a chair at the Media Lab called LEGO Professor of Learning Research and Papert obviously became the first LEGO professor. In 1998, after Papert became professor emeritus, the name of the professorship was modified, in his honor, to the LEGO Papert Professorship of Learning Research. The professorship was passed on to Papert’s former student and long-time collaborator, Mitchel Resnick, who was known for creating the Scratch visual programming language. Resnick continues to hold the chair today.

For the last 20 years of his life, Papert lived in Maine, where he founded a small laboratory call The Learning Barn. He spent a large part of his time working in the Maine Youth Center in Portland, the state’s facility for teenagers convicted of serious offenses.

A Theory of Learning, not Teaching


Most people are more interested in what they learn than how the learning happens. In fact, most learn without giving a thought to learning. I often go to the other extreme. I learned to juggle, to fly a plane, and to cook, not only because I wanted to do these things but also because I wondered what the learning would be like.(Children’s Machine, 29–30)

One thing I find immensely resonating is this lifetime interest in learning. It is not about what you learn; it is about a genuine interest in how learning happens. I have spent a large part of my own life learning things. Not all of them are practical — in fact, very few of them are. For example, I have spent a lot of time learning foreign languages. In the last decade I have tried to learn French, Spanish, Italian, German, Japanese and Latin. I wasn’t always successful in these linguistic pursuits. And I don’t even think I am especially talented in language acquisition. Some of these pursuits are almost entirely futile as I didn’t get to practice them often. I have a passion for learning languages not because they are useful skills, but because I feel amply rewarded doing so.

Learning a language is an interesting thing. Yet do we have a good theory as to how to do it? On the one hand, we know that everyone, regardless of education and intelligence, is capable of learning a language — the mother tongue. On the other, learning a foreign language is seldom an easy thing to do. Having grown up in China I experience firsthand the futile kind of English education in Chinese grade schools. Perhaps the same can be said about French in American schools.

What makes learning a foreign language at school so difficult? Surely that is not because there is a lack of pedagogical theories? What is the secret behind effortless native language acquisition? How can we learn something without even being taught?

THIS, I think, is the difference between a theory of learning and a theory of teaching. A theory of teaching, or of instruction, treats knowledge as something that can be communicated, in its abstract form, from one person to another. A theory of learning, however, emphasizes the process through which we assimilate concrete things and endow them with unique meanings. It describes an experience that is much less structured, more ad-hoc, and, most important of all, self-directed and extremely concrete.


learning theories

The idea of self-directed and concrete learning is central in Papert’s learning theory. It is reiterated throughout his three books. Yet it is also a point of contention and easy to misunderstand. To put the idea in its most radical form, some may say that we all learn to speak our mother tongue because we are never taught to do so. Does this mean students are better left alone, without instructional preparation and guidance? Of course not. There is always teaching involved, even in the native language acquisition, as every parent would know trying to teach the baby to talk. And this is the same case with the vast majority of things we learn. We learn mostly because we are shown how it is done. Even when we say “self-taught” we are still relying on past experiences of how we learn things and other non-human resources to help us learn. In a sense, teaching is unavoidable. But we need to be mindful of what teaching potentially subtracts from learning:

…every act of teaching deprives the child of an opportunity for discovery… the goal is to teach in such a way as to produce the most learning for the least teaching. (Children’s Machine, 139)

What does this mean? It means that the way you teach something is not necessarily the best way a student learns it. So by forcing the student to see things or doing things in your way, you are depriving them ways to make things their own. Think about drawing lessons: if you teach the student to draw exactly what you draw, is that the best way to learn to draw? While drawing does involve mastering some techniques, isn’t it mostly about how to see things in your own way and express your own feelings?

In practice, I find the “least teaching” approach definitely not the easiest way out. In fact, it might be the hardest. It asks you to do more work, to be vigilant about not only student reactions, but also your own conceptions of things. Compare to this kind of mental work, deliver the class along a ready-made schedule is way easier. This is what people do in all sorts of enrichment classes: download a curriculum, print it out, and you are ready to go.

This said, I think it is fair to say that different subject matters demand different learning methods. There are certain subjects that lend easily to the self-directed learning style; and there are other subjects that are less so; for these subjects lecturing and discussion is still the most effective method.

Papert’s favorite subject is of course math. He told us many times about how he became fluent in the math language, because he was able to experience math in a different way than the many other unfortunate kids. He learned to speak math while living in a mathland. He then drew from this experience and tried to apply that success story to other subjects, such as programming. A central problem he tried to solve, is how do we teach programming to kids.

How do We Teach Programming Today


I say “how do we teach”, but I should have said: “how do we make kids learn”. In fact, the best way to put it would be: how do we make them speak a new language that is also spoken by the machine?

It is unnecessary to reiterate here the claims and rationales of today’s numerous CS curriculum proponents. Suffice to quote Steve Jobs, who was not known for coding aptitude or any kind of technical work, on this matter. He said in a recently surfaced interview,

“Everybody in this country should learn how to program a computer, should learn a computer language, because it teaches you how to think.”

Jobs isn’t starting all this craze for kids coding; he is just endorsing (or being used to endorse) the craze that is already underway. An announcement from the white house regarding the “computer science for all initiative” claims that “Our economy is rapidly shifting, and educators and business leaders are increasingly recognizing that CS is a ‘new basic’ skill necessary for economic opportunity and social mobility.”

Of course, we are not there yet. The same announcement continues with, “By some estimates, just one quarter of all the K-12 schools in the United States offer CS with programming and coding, and only 28 states allow CS courses to count towards high-school graduation, even as other advanced economies are making CS available for all of their students.”

There are reasons to be optimistic, however, that CS-coding will eventually find its place in the already busy grade school curriculum. Because there is a demand; and for many, the demand is increasing faster than we can fulfill it. Numerous online resources are there for this very reason. People want to work in tech companies. And every tech company needs a couple of brilliant programmers to do the dirty work. If coding is a great way to retool yourself for career changers, it should also be a good thing to know before it is already too late.

Well, Seymour Papert started all this. Back in the 1960s.

It is important to remember that at Papert’s time, when he invented the LOGO language and started to try it out at schools, there was no Google, no Facebook, or any of the tech giants today who made it clear that the ability to code will land someone in a well paid job. If Papert wanted to teach kids coding, surely he wasn’t thinking that the activity may add some exciting prospects to the kid’s future career in the high tech section — the only sections that actually hire programmers at the time were the military and government. Taking coding classes won’t help your college admission or count as Advanced Placement class either. Yes, you can do coding (at selected locations if you are lucky), but do it at your own risk. Your parents are not going to send you to coding Boot Camps and pay for those expensive lessons!

It might be said that in Papert’s time, coding was not an activity that adults felt the urgency to do, although the kids’ reaction is strikingly similar. Looking at how coding is taught today, one cannot help but to wonder what we have learnt since the 1960s. For despite parent endorsement, great financial resources and promises, we have made very little advances in terms of coding pedagogy. Yes, we have more in-school and after-school programs; there are a lot coding tutorial and online courses dedicated to programming languages. But we are far from cracking the problem of coding education.

For a recent example, look at Apple’s Swift Playground on iPad. It is the kind of enterprise that on the one hand, took a lot of thing about learning for granted and on the other, made great effort on trivial matters.

This is why talking about Papert is still relevant today. Because how to teach programming is what he cared about; and without all the fuss about how this learning a practical skill can lead to future career success, his motivation is so much purer. It is really about learning how to acquire a way to think, not about acquiring a combat skill for future survival! We may not have a perfect solution to the problem of coding education yet. But the first step, if we follow Papert’s thinking, would be to find, or invent if necessary, a perfect language for learning, instead of sugarcoating BASIC with cute animations and cool looking interfaces.

Situated Cognition


The role of the teacher is to create the condition for invention rather than to provide ready-made knowledge. ..creating a learning environment in which there is no direct teaching at all. (Connected Home, 46)

Many of Papert’s intuitions have been confirmed by a branch of modern cognitive science called situated cognition studies. This school (if it can be called such) believes that human understanding is not primarily a matter of applying abstract and general principles to objects and situations. In fact, it believes that knowledge is always situated, meaning it is intimately associated with the context or culture in which it is produced. It argues that comprehension (a prerequisite to learning) has to be grounded in perceptual simulation that prepare for situated action. Here “perceptual simulation” doesn’t mean you hear your teacher talking in the distance; it means an active and continuous perceptual feedback given in an act of active exploration of a concrete world. In other words, you have perceptual simulation if you are using your eyes, your hands and the rest of your body to solve a problem that really interests you.

In a traditional school curriculum, this is unnecessary. Even worse: it is an undesirable act as it creates chaos in the classroom. When I was in grade school, the teacher would ask every student to sit tall and put both of our hands behind our back. An optimistic view would be: it is as if we were soldiers waiting to be inspected; or you can say these teachers really want to tie us onto some imaginary stick so we can’t move our hands or wiggle. I can’t say how well this strategy worked for my teachers, but they were certainly successful in giving me a mental image of an impeccable classroom, where rows and columns of pupils, like tree saplings, are dying to be irrigated by the elixir of knowledge. I cringe, therefore, at the sight of kids sitting on a rug, loosely surrounding the teacher in today’s classroom. I envy them, but I also can’t help to doubt: are they going to give full attention to their teacher sitting like this?

The key to any classroom learning is attention. But the traditional way to get that attention is through something called “order”. When the link between learning and attention is forgotten, we arrive at the following shortcut: the best classroom is the one that has the most order. Order needs to be enforced; it is not in kids’ nature. Therefore, strategies need to be devised; commands need to be issued; disobedience, punished.

The art of teaching, in this scenario, consists of doing a great presentation of the subject matter. To this day, it involves being eloquent, using lively analogies, using gestural and graphic illustrations and constantly soliciting interactions in the form of raising questions for students to answer. I do not wish to deny or reject this venerable pedagogical form. It worked for me, and it worked for schools and kids from the beginning of time. And I regret to say that this is precisely what digital online learning has been busy duplicating. But Papert’s thinking points to a different direction. It points to how “deep learning” — a term we need to invent so as to differentiate it from the regular learning — can happen.

As Papert’s personal story of how differential mechanism facilitates the development of his mathematic thinking demonstrates, learning can be greatly aided by associating abstract principles and operations with concrete objects that have specific meaning and emotional attachment to the learner. Renowned computer scientist Marvin Minsky wrote in his Society of Mind:

The secret of what anything means to us depends on how we’ve connected it to all the other things we know. That’s why it’s almost always wrong to seek the “real meaning” of anything. A thing with just one meaning has scarcely any meaning at all (Minsky, 1987 p. 64).

What does it mean by “connected it to all the other things we know”? It means that, to a particular person, the meaning of an object, event or idea is what that person has done, will and can do with the object, event or idea. This doesn’t mean we never get to an abstract understanding of any subject. But we arrive to that point through enumerating a considerable amount of concrete instances of the matter. Hands-on experiences facilitate learning not only because it is concrete, but also because it is personal; it personalizes the meaning of abstract principles by establishing an experiential relation with them. This is what happened to Papert in his encounter with the differential gears. Papert emphasizes that he didn’t mean to call for a similar fascination with gear box in every child who needs to learn math. But the example shows that what such an emotional engagement can transform a subject generally considered abstract into something that is highly concrete.

The concrete side of things have immense power, because this is how human mind is built. This is not to underestimate the power of abstract thinking. But to think that learning is about replacing concrete thinking with abstract ones is a mistake. And so is the idea that abstract thinking is superior because it is what only adults (at least some of them) can have. Papert stresses the fact that even in the most serious intellectual endeavors (such as those of a mathematician) there is a place for concrete thinking. This is also where he feels he has diverged from Piaget’s widely influential idea that cognitive development can be divided into four successive stages (sensorimotoric, pre-operational, concrete and finally formal). This somewhat implies that reaching a higher stage will upgrade the brain to a newer, more advanced kind of machine. An idea that is probably closer to truth is, all these previous modes of thinking still exist, but are now layered underneath abstract thinking, not because the latter is the most powerful mode, but because that is what our education system champions throughout its course.

But how could we have a concrete experience learning abstract concepts? Isn’t there a limit to how we can concretize and personalize a subject of study? Here the computer comes to the rescue; literally a deus ex machina. Computer concretizes everything that is abstract. A computer can do that, because its essence is simulation.

Perhaps the presence of computer in today’s society has become prevalent that we no longer feel the thrill. It is for this reason that revisiting the very moment of computer’s introduction to education becomes illuminating. Papert’s work presents precisely such a historical document of how educators thought of computer when it made its first appearance in schools. Sherry Turkle’s The Second Self complements this nicely by presenting how the kids reacted to computer when they made the first encounter. Incorporating extensive material in clinical precision and rhetoric elegance Turkle made a highly lyrical yet precise snapshot of what computer meant for education in the early 80s.

Computer and Education Reform

Papert recounts how, in a visit to Cyprus in 1965, he first thought about how to make the computer do the same things to children that happened to him since he came to MIT. This is the basis of his thinking, something that guides his understanding of what computer can do, especially regarding its potential in learning. I want to summarize Papert’s thinking with three main ideas here: free form exploration, problem-solving through concretization of knowledge, change in thinking in non-computer related areas. And I would argue that these three ideas can contribute immensely to the problem of computer and learning.

For Papert, the students learn to program, not because it is a highly desirable skill that would secure a future career (we need to be reminded that that was NOT the case in the 1960–70s). They learn to program a computer, which helps them to learn other things. Playing with computer in the form of programming boosts cognitive skills. This is why Papert was excited about having more computers in the classroom. Learning to code is not only for better coding , but for a general benefit in learning all subjects across the school curriculum.

This dream of Papert’s is very far from being realized. And the reason is not that we do not have enough computers in the classroom (although we can certainly use more) or that people are not interested in teaching kids how to use computers. Computer has not played a decisive role in education, because we are still not clear in what it can do, let alone to change the face of education.

Papert was a pioneer in recognizing the potential of computer in education. But his approach was markedly different from other pioneers in the same area. One such figure is Patrick Suppes, a philosopher by training and longtime Stanford professor. In the 1960s, where computer was still a barn-sized monster, Suppes envisioned a future where all children were granted unlimited access. “In a few more years,” he predicted in 1966, “millions of schoolchildren will have access to what Philip of Macedon’s son Alexander enjoyed as a royal prerogative: the personal services of a tutor as well informed and as responsive as Aristotle.” This vision is now known as Computer Assisted Instruction (CAI). Although Suppes’s ideas were somewhat preceded by the development of PLATO (Programmed Logic for Automatic Teaching Operations) system at the University of Illinois in 1961, the general idea to use computer as a tutor, which helps its tutees in the most classic form of drills and practices, is now closely tied to Suppes’s name.

Papert acknowledges Suppes’s seminal approach,

The concept of CAI, for which Suppe’s original work was the seminal model, has been criticized as using the computer as an expensive set of flash cards. Nothing could be further from Suppe’s intention than any idea of mere repetitive rote. His theoretical approach had persuaded him that a correct theory of learning would allow the computer to generate, in a way that no set of flash cards could imitate, an optimal sequence of presentations based on the past history of the individual learner. At the same time the children’s responses would provide significant data for the further development of the theory of learning. (Children’s Machine, 164)

Yet he also insists that he has to reject it due to “gut-level response” (164) because:

…Behaviorists are fond of using the designation “learning theory” for the foundations of their thinking, but what they are talking about is not “learning” in the sense of something a learner does but “instruction”, in the sense of something the instructor does to the learner. 164

Now, Suppe’s, or should we say the behaviorist approach, has become the foundation of instructional design in corporate setting, where the so-called “learning” (a better sounding word than “training” I suppose) is divided into bite-sized units and delivered, proudly, on major mobile platforms (as if the simple fact that we see something on iPad would facilitate learning!). These pad-sized “flashcards” — indeed they are no longer that expensive — provide rote drills that the corporate learners feel acceptable, because 1) it is convenient 2) it is somewhat rewarding if some sort of gamification is involved 3) the drills have a beneficial contribution to career development. Added to this there are extensive parasitic functions that track every process, register them in a database, and then generate easy to understand graphs and reports for the management, who paid for the system. The system that integrates aIl these is referred to, somewhat paradoxically, as Learning Management System.

What Papert has against Suppe’s approach, or the behaviorist approach in general, is precisely that such a process should be distinguished from the kind of learning that he is interested in. While the word learning evokes for Papert freedom of exploration, acts of self-empowerment, the bricoleur spirit and transformation of one’s perception of the world, corporate training is none of these. It really is a rigid set of facts or some procedural knowledge that the management wants you to memorize, so that you can increase your productivity, or that otherwise you would make mistakes. The last point is important: in a true learning process making mistakes is a positive thing; it is a sure indication that learning is happening.

The unfortunate reality of technologically expediated rote drill prevails also in the world of younger learners. The same dire setting is found in numerous softwares that cater to the need of grade school curricula. There is no need to name names as nearly all of them work the same way: you create an account by which your interaction with the system will be recorded; you do one drill after another and in the end the system will let you know how many questions you have done right. To make this less boring we sugarcoat it with cute animations, sound effects, stars and badges and other unlockable goodies.

Papert went on great length to articulate how he needs to reject this approach. Although I have great sympathy with him, my take on the matter is slightly different. I think rote drills are useful. Every elementary school teacher knows this as a fact. And if computer can help make it more fun, why not? Just don’t call it learning. Because learning needs to mean something else!

I think if we need to listen to Papert, it is not because he offers something immediately practical, something that we can use to replace the current system wholesale. He holds rather an idealist notion that computer has to do something different, or even revolutionary. One thinks of the Apple 1984 ad campaign, where personal computer is seen as a unstoppable force to break the Big Brother’s omnipresence eyes (but Big Brother also knows how to use computer, and much better than us!). Yet an idealist notion of computer and education such as Papert’s is precisely what we need today where the word learning is juxtaposed with management and nobody would raise an eyebrow.

What is Computer Literacy?

The kind of knowledge children most need is the knowledge that will help them get more knowledge. (Children’s Machines, 139)

Now that we have lots of computers, and an ever bigger and more ubiquitous internet, several issues emerge. One: using this new technological resource, what can we do to enhance learning? Second, has learning about computer become necessary for everyone? If so, how much do we need to learn?

The first question can be termed the “digital learning” question while the second “computer literacy” one. These are questions of great importance now, and their relevance will only increase in all future societies. But to get a clear understanding of this question we need to go back to the past, to the moment where computer was first introduced and the internet was nowhere in view.

Papert’s first official encounter with computer was the British ACE (Automatic Computing Engine) designed by Alan Turing himself (which Papert might have accessed in his Cambridge days). But one earlier encounter, according to his own accounts, was even more meaningful: it was a computer that he made himself in the 1940s. This computer (he didn’t tell us what exact that was) obviously couldn’t do anything other than demonstrating the idea behind a computer. But it was a quality learning experience — perhaps much more so than using a computer bought at store loaded with all sorts of edutainment softwares.

In the 1960s time-sharing OS and tele-text terminal were developed which allowed the expensive mainframe computer to be accessed remotely, and by multiple users simultaneously. The computer is already “connected”, at this point, only to be disconnected by the advent of personal computer. But the lost of connectivity is more than compensated by the exponential increase of computing power (than the dumb terminal). It took another three decades for telecommunication technologies to catch up and connect these isolated computational islands, a process that remains unfinished to this day.

The rise of the internet is certainly an integral part of the history of computer. Yet when we talk about learning, I feel we need to make a distinction between the role played by computer and that of human beings behind computers. A computer is a machine that does computation in front of you, but it can also retrieve information from other connected machines. As the history of computing shows, networked computing was there first and you can say computation is always already a networked affair. But people behind the machine is a different matter. They generate content, but this is not something they only started to do with the advent of internet.

Back to the issue of how computer age facilitates learning, there are some arguments that tend to confound the machine’s and the people’s contribution. James Paul Gee for instance uses the phenomenon know as pro-ams to advocate digital learning. Pro-ams refers to the case where amateurs develop skills in certain areas (video production, machinima, fan fiction, game related design, etc.) and become experts in these areas. The argument goes that this kind of learning is only possible in a society connected by digital technologies. As such it is a “medium specificity” of digital learning.

In my opinion, however, this is probably not the strength of digital learning per se. It is merely an immediate benefit of the wide availability of virtually free media tools, anonymity, devoted communities and strong individual passion. None of these is really dependent on the presence of computer. If someone had lived in a closely knit masonry neighborhood where tools and material are free to use and extremely friendly people are ready to help, we shouldn’t be surprised to see no formal schooling is necessary to make a great mason: one learn the trade by practicing it with passion, and with the help of community. An academic jargon here is “cognitive apprenticeship”, which is a theory of learning that champions the presence of a master and lots of guided hands-on as a model of learning. There is nothing decidedly digital about this process. The digital technologies may magnify the effect and obliterate the physical requirements. It may help a disabled old woman to engage with a virtual community and learn from them the craft of designing objects for The Sims, but it is a form of learning that has existed in human society for thousands of years. In fact, the hunter-gatherer society relies exclusively on this home-community style learning, whereas school is really a much recent invention, the result of the industrial age.

I do not wish to deny the usefulness of internet as an inexhaustible content reservoir. The internet has become a must for virtually all modern learning infrastructures because it offers ways to build and maintain communities of learning. There is also the technical sense that the internet connection is required in order for the learning software to function, as a lot of them are running on remote servers and implemented in the web browser — the thin client is an attractive option but not always working. But the ways in which computers can foster learning is not necessarily dependent on the presence of a virtual community. Nor does it have to run remotely to be effective. Knowing that today the two are often intimately intertwined, we nevertheless need to discuss them separately.

A less ambiguous but equally controversial term is “computer literacy”, or sometimes “computational literacy”. These terms actually mean different things. For example, computer literacy can be regarded as the ability to use computer to perform a reasonable amount of tasks. Computational literacy, however, put an extra emphasis on coding because it argues that simply using computer would be the equivalent of reading, not writing.

Digital learning, as how Gee would define it, seems to be tied to the former sense of literacy: the ability to use computer to do things, mainly creating digital content and artifacts. I have seen plenty of people who achieve masterful skill in doing this yet admit knowing nothing about how computer works. Nor are they interested. I find it perfectly fine if a person can make wonderful custom levels for Tomb Raider, but nothing about programming languages.

However, if we use the original meaning of literacy — the ability to read and write language — we see that the two are almost always taught together. This gives a good reason for advocates of the latter, stronger sense of literacy: in order to possess real literacy, you need to know how computer works from inside out, and to actually write code to mesh with computer’s workings.

Yet that would be an unfair analogy. One might point out that reading and writing involve, at least in the case of alphabetic languages, a similar set of cognitive skills. I say similar set, knowing that there is a huge difference in terms of the ability to write between a kindergartener and a Nobel Literature laureat. But in order to be called a literate person, you don’t need to be a good writer. Most college kids write terribly; yet they are literate, or even, “well educated”. You don’t need to actually practice this skill very often beyond simple text and twitter messages!

If this is acceptable, why do we have to teach kids coding?

Going back to my Steve Jobs quotation: “Everybody in this country should learn how to program a computer, should learn a computer language, because it teaches you how to think.”

But here is the rest of the quote:

“It’s like going to law school. I don’t think anybody should be a lawyer, but going to law school can actually be useful because it teaches you how to think in a certain way…I view computer science as a liberal art.”

I find this last statement extremely fascinating. I am not aware of anybody else who has quite the same view: computer science as a liberal art! Those who promote coding education either speak from the viewpoint that this is an extremely useful job skill — liberal art is NOT, or they implicitly believe computational thinking is a superior way of problem-solving (I disagree).

My take on the issue is this: computer points at a certain way of thinking. So as long as it enlarges your cognitive horizon and equip you with a piece of powerful tool, it should be encouraged. It will benefit those who put in the hard work. On the other hand, to really learn computational thinking to the point of being able to write good code, is a big commitment. Not everyone needs to make that kind of commitment and it would be a damage to the diversity of thinking in our society. So the short answer is: yes, we do need more programmers in the future; no, we don’t need everyone to think this way.

Papert’s Legacy


Gary Stager, who worked with Papert in his Maine days and now curates a website called Daily Papert, verbalized this uneasy feeling regarding Papert’s legacy:

While Papert’s scholarship is widely recognized, his half century of contributions is largely invisible. It is not that educators disagree with him, they just ignore him entirely, and he is absent from teacher education texts, and school reform literature.

Why is the man ignored? I think there might be three reasons:

  1. Should it surprise us that we are an amnestic race?
  2. There are people interested in coding education today who are almost embarrassed to see the rudimentary graphics in the many Logo examples provides in his books. They jump at the conclusion that since the software is dated (all software must be) his ideas must be, too.
  3. There is a lot of misunderstanding regarding his ideas. One reviewer of The Connected Home said,

Children will learn more, given the chance, by random exploration on a computer than by directed lessons or “educational software.” There, I’ve saved you the trouble of reading “The Connected Family,” which does little more than play variations on this tune for 200 pages.

Does Papert suggest that children are better off doing random things with computer? I don’t think so. If that were the case, why bother designing a new language just for learning? He is not even against directed lesson. But it is understandable why some readers may have that kind of reaction. Self-directed learning was a new concept then as it is now. We have not yet cracked the problem. So on the surface we may be seen rejecting everything without offering a perfect alternative. This is especially true when the ideas one tries to implement are of a highly original and radical nature.

Papert is an honest and original thinker, and his writing is full of valuable insights. But all writers struggle to articulate their ideas in a clear, well organized way. I see those efforts in Papert’s writing and I somewhat regret he didn’t keep up with the pace of the rapidly evolving technology. But this doesn’t give us reasons to neglect his pioneering efforts. If in the 1970s, the appearance of personal computer marks a first wave of bringing computers to schools, right now we are witnessing a second, and a much bigger wave of prompting computational thinking in schools. And it is the movement’s lost that it seems to have forgotten much of the educational research conducted since the first wave.

This is why we need to bring Papert’s ideas to more discussions. We need to regenerate those ideas with new technologies. AND WE NEED TO DO IT NOW.

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