At MIT, an innovative group of artificial intelligence (AI) researchers began to propose intriguing answers. Their efforts would produce not only a new programming language, but educational strategies that would profoundly influence how Americans taught programming for years to come. The leader of this group was Seymour Papert (1928–2016), a mathematician and psychologist who co-founded MIT’s AI laboratory with Marvin Minsky (1927–2016). Papert co-developed the Logo programming language and launched what became known as the constructionist movement in science education. Artificial intelligence (AI)AI.Artificial intelligence (AI)Constructionist movement in science education
After receiving a Ph.D. in Mathematics at the University of Cambridge (1959), Papert studied for 5 years with psychologist Jean Piaget (1896–1980) at the Center for Genetic Epistemology in Geneva, Switzerland. Papert came away impressed by Piaget’s way of seeing children as active builders of their own intellectual structures. Papert came to believe that children readily used the materials that they found about them to learn, and that these tools were most efficacious when they were a regular part of the surrounding culture.36 For Papert, such a tool could be the computer, if it could be adapted to the educational aims of teachers and the natural experience of students.
Papert and his colleagues began to ask important new questions about learning to use computers in public settings. How might computers affect the way that people think and learn? Can computers be carriers of powerful ideas and the seeds of cultural change? How can computers help people form new relationships with knowledge that cut across the traditional lines separating science from the humanities? Can people of all ages learn computing principles? If the goal of teaching about computers is cognitive development, what is the ideal age to start young children?
Papert was particularly interested in using computer programming to enhance cognitive development. However, the psychologist was unimpressed with the way that people were learning programming skills in his day, and he believed that the current crop of programming tools and primers were only suited for adults. A mathematician by training, Papert believed that computers could revolutionize math instruction if coding tasks were more naturally connected to a child’s developmental impulses. His MIT group responded by creating Logo, a high-level language and system that utilized visual output (computer graphics) and automated devices (robotics) to teach programming. The Logo language was co-developed by Seymour Papert, Cynthia Solomon, and Wally Feurzeig in 1966, and the system made its debut in Cambridge, Massachusetts Feurzeig, Wallythe following year. Within a decade, Logo became the leading educational computer language in the U.S., rivaled only by BASIC in high schools and Pascal in university settings. Pascal
Logo’s most iconic learning feature was the turtle, an on-screen shape that showed the result of the language’s movement commands. Some implementations of Logo also featured an animated Turtle Writer robot (see Figure 3.11), which students could program to draw shapes and move around the classroom. (The robots were also equipped with sensors that allowed them to avoid obstacles and learn from their environment.) When programmers typed in Logo commands at a terminal console (or later, on a PC keyboard), the commands produced line drawings and other shapes that the turtle could create—the so-called “turtle graphics.” Turtle graphics
Because Logo is an interpreted language, each command is executed as soon as the programmer enters it, and users are able to see the output of their program statements immediately. Papert and his colleagues used this interactivity to create a hands-on world of block-building and experimentation that (for them) characterized early childhood development, particularly formative experiences with spatial reasoning and mathematics. Later on, more conceptual programming concepts were introduced with additional language features. In fact, the MIT team highly valued the “pre-programming” tasks associated with computer programming—the problem solving, algorithm building, and skill self-assessment that they believed promoted deep learning. Papert found the process of testing and debugging programs to be particularly instructive, because it gave students immediate problem-solving practice when things went wrong. “Pre-programming” tasksTestingDebugging
Figure 3.11Seymour Papert at MIT with a Turtle Writer robot and a fish shape that it produced. (Image courtesy of the Computer History Museum)
Papert’s goal was to make computer programming an immersive process. In his book Mindstorms, he described his objectives in relation to human language learning:
It is possible to design computers so that learning to communicate with them can be a natural process, more like learning French by living in France than like trying to learn it through the unnatural process of American foreign-language instruction in classrooms… The idea of “talking mathematics” to a computer can be generalized to a view of learning mathematics in “Mathland”; that is to say, in a context which is to learning mathematics what living in France is to learning French.“Talking mathematics”37
Rather than using a computer to program the child, so that the child might learn to mimic the computer’s ways, the child should program the computer, acquiring a feeling of mastery over the device, developing a sense of agency from intimate contact with the technology.
Papert’s ideas had political and economic consequences, because he recognized that American schools had limited access to computers and time-sharing systems in the 1960s. (In fact, it would be challenging to provide children with even limited access to computers through the 1970s and 1980s.) But Papert’s concerns about access and the social conditions for learning echo calls for universal tools and the “convivial technology” that we observed in the writings of Ivan Illich, Stewart Brand, Lee Felsenstein, and Ted Nelson in Chapter Convivial technologyBrand, StewartFelsenstein, Lee2. Indeed, although Papert’s work is not usually framed as “countercultural,” his circles shared many sympathies with countercultural technologists in Europe and the U.S. In the following years, educational specialists in Britain would introduce computers to children in what they called infants school and primary schools. (See Figure Infants schoolPrimary schools3.12.) There was also an early relationship between the Logo team at MIT and the research group at Xerox PARC in the San Francisco Bay Area. Daniel G. Bobrow wrote the first version of the Logo program in Lisp while working in the AI group at MIT. In 1972, he moved to Xerox PARC and worked there for several decades. Cynthia Solomon also worked for Apple and Atari in the 1980s, overseeing implementations of the Logo language for PCs. Lisp
Logo was created in the research labs of MIT and Bolt, Beranek and Newman (BBN), but the language had its greatest impact after PCs made educational software more accessible to students. Planning for a long future with technology was always part of Papert’s vision, and he worked to establish pathways between computers and education all his life. “My discussion of a computer culture and its impact on thinking presupposes a massive penetration of powerful computers into people’s lives. That this will happen there can be no doubt.”Bolt, Beranek and Newman (BBN)BBN.Bolt, Beranek and Newman (BBN)38
Papert’s colleagues, Wally Feurzeig, Cynthia Solomon, and Daniel Watt, were also instrumental in disseminating the group’s ideas into the community. Wally Feurzeig (1927–2013) had a 50-year career at BBN in Cambridge, where he specialized in AI research and the interactive use of computers in schools. In the early 1960s, Feurzeig was interested in time-sharing systems and interpreted computer languages, and he envisioned these technologies working together to make learning easier for students. Feurzeig created the TELCOMP computer language in 1964 to teach elementary mathematics through TELCOMP computer languageprogramming, followed by the Stringcomp language that supported algebraic expressions and higher-level