A generation ago, computers only understood text. You would program the computer in English text. You would ask your questions on punchcards encoding text. Your answer would be provided as monospaced, unadorned text. Since the early 1980s we have refined the graphical user interface, or GUI, to allow humans to communicate with computers on more familiar terms. Although a boon for the layperson, GUIs have been troublesome for computer scientists. They are hard to build because they are so open-ended. They are hard to test, because rather than printing a single correct answer there are many paths the user may take to accomplish the same goal.
Computer science still starts with a text editor and a compiler, because programming is better served by text. Text affords programmers absolute control over their programs. Written language is far more expressive than pointing and clicking, allowing for a explicit and precise descriptions. Clean code is a clear explanation of an algorithm directed to a mindless worker. The struggle of a programmer is to achieve sufficient clarity for both the computer and him- or herself. It can be a very enlightening experience, to debug an algorithm and then discover it doesn’t quite do what you wanted it to do, and so adapt it further. That said, the sheer austerity of the task can make it daunting without the right training and motivation on the part of the programmer.
GUIs are quite the opposite. They show many available options, reward experimentation, and make complex actions easy by hiding detail. GUIs make computing accessible to a wide audience. A user interacts with a GUI as a peer, clicking and dragging and seeing how the interface reacts. Ultimately, convinced the GUI is logical and predictable, they embrace it as a new way of thinking. But GUIs are limited. They make it very difficult to perform analogous actions repeatedly or store a sequence of actions for later use.
There is an analogy to be made with education. Programming is like direct instruction, where knowledge is relayed linguistically and authoritatively. (No wonder Bill Gates and Salman Khan like it.) GUIs are like constructionism, where feedback loops reveal non-arbitrary behavior of a system that the user/student slowly begins to internalize. (So I constructed my own definition. How meta.)
Both methods of interacting with a computer are valid and potentially productive, so it seems both educational philosophies are valid as well. But there is a critical flaw in the analogy. For GUIs, students are analogous to the user and the computer is akin to some representation of the material itself: manipulatives, an experiment, a video, a graph or plot. But for text-based programming, the student is not the programmer; they’re the computer! The teacher is the programmer, the direct instructor, who crafts clear explanations of algorithms for the students to mechanically follow.
Direct instruction is degrading. It robs them of their ability, desire, right to explore and create. Knowledge transfer is not like copying a file, where we wait as it is methodically duplicated. Knowledge is personal, with idiosyncrasies and unique contexts. To insist on teaching children the same way we program a computer is simply wrong. It cuts to the core of what Dethorning STEM is about: our society treats people like computers and computers like people.
On a positive note, this analysis suggests that we should introduce computational thinking as another way for students to interact with the material in a constructionist setting. Having students write their own psuedocode for long division may be a viable way to teach it, if it needs to be taught at all. Computational literacy will play an increasing role in the next century as computers become more ingrained in out lives. In the future, following an algorithm won’t be good enough — you’ll have to be able to write one.
Unfortunately, the state of computer science education is in shambles. Basic computer classes often teach how to use Microsoft Office by following rote algorithms — truly the blind leading the blind. Computer science itself takes a back seat to all other subjects, and is only sometimes offered as an elective. But I think that computational literacy does not require a computer scientist, a computer lab, or even a computer. It’s not content; it’s a technique. By cleverly inserting the right activities into the existing curriculum, teachers can cover computational thinking alongside any subject. Training teachers how to do that, and getting the administrators to sign on, will prove difficult.
A new, innovate approach is needed. One that breaks from the ossified red tape and small scale of the classroom and equally from the poor pedagogy underlying of most edtech products. The next generation of children deserve no less.
