Archive for September 2012

Learnable Processing Programming: Part One

Brett Victor recently posted an intriguing essay entitled Learnable Programming. I’ve been a fan of many of his essays (e.g., [1] and [2]). Victor is quite a pioneer of what we might refer to as design-based computing pedagogy as opposed to a programming or assessment driven pedagogy. And this essay surely lives up to his previous work; I think I could write fifty blog posts reacting to this single essay. T-49 …

Pedagogical Processing

I use Processing in my introductory computing class at Bard. This semester is my third iteration of the class. I chose Processing for a variety of reasons, some of which overlap:

  • Processing allows artists and designers to write real code with relatively little pain.
  • Processing balances developing powerful programmed artifacts with a relatively simple programming interface.
  • Processing has a welcoming, productive open-source community.
  • A ton of libraries and examples help students write relevant, meaningful sketches (e.g. with video, images, sounds, etc.)

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On Computational Teaching

Why aren’t computer scientists the best teachers?

(Or, why our students aren’t robots.)

“Science is knowledge which we understand so well that we can teach it to a computer; and if we don’t fully understand something, it is an art to deal with it. Since the notion of an algorithm or a computer program provides us with an extremely useful test for the depth of our knowledge about any given subject, the process of going from an art to a science means that we learn how to automate something.”
—Computer Programming as an Art, Donald Knuth, CACM, December 1974.

Why aren’t computer scientists better teachers? Or rather, why aren’t computer scientists the best teachers? We are very good — pros, in fact — at explaining things in absolute detail to things that know absolutely nothing. If we can explain quicksort to a RISC machine, we should surely be able to teach binary-search to teenagers, right?

But of course we don’t teach our computers. And we don’t program our students. Or do we?

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The Algorithm Made Me Do it!

Last Thursday, one of our Live Arts Bard artists-in-residence, Annie Dorsen, gave us a behind-the-scenes view of her latest production: False Peach. The performance explores the notion of consciousness, agency, and language through an algorithmic production of Hamlet. Although still in its infancy, the play will use automated dialogue and lighting to recreate the famous play in an experimental fashion. I had the chance to attend her presentation of her piece Hello, Hi There at AAAI-2012 in Toronto this past summer. Hello, Hi There will be performed at Bard in November.

The moral agency of robots, or software-bots, has been a hot topic in both philosophy and robotics lately. And from my perspective, the most interesting part of Annie’s work, is this notion of agency or control. Who is responsible for the software’s actions? — whether it be drones or drama. In some ways, the creators of the play relinquish control of the production to the algorithms. They hit start at the beginning of the performance, and magic happens. Every night is a different, unpredictable performance. Random number generators, equations, and conditionals guide and mis-guide the narrative. But in an important sense, this type of production is one of the most controlled plays ever. Everything is predetermined, and nothing is left to human improvisation. There is no pesky spotlight operator to miss her cue or actors to make a slightly late entrance or improvise the delivery of a line. And not only is it deterministic and in some ways eternal, but that stems from a human encoding this knowledge in a computer program. It isn’t eternal because it’s a machine, it’s eternal because the play is formally encoded.

I asked Annie a question relating to this notion of control and she had a great response about this work truly investigating the notion of collaboration. Collaboration between humans, and collaboration with machines. By formalizing the dramatic decisions in code, a very systematic exploration of collaboration and decision-making is possible. A very exciting approach to exploring agency, and I look forward to the final production next year.

Robots Make CS1 Real.

My colleague in the computer science program at Bard, Sven Anderson, is teaching our introductory computing class. The course teaches object-oriented programming using the IPRE robot platform and python. And although robots are error-prone with their noisy sensors and motors, eat batteries, and make it difficult to grade student programs, few pedagogical approaches to CS1 can provoke the following reaction:

Photo courtesy of Sven Anderson.

Truth Values: What Mathematics Can Teach Us About the CS Gender Imbalance

Tonight we took a group of Bard students to Vassar to see the play Truth Values by Gioia De Cari. The one-woman-show depicts her “Romp Through M.I.T.’s Male Math Maze.” It was a funny, touching account of the strange world of mathematics research and graduate education — first encounters with the research kind. The play made me reflect on my own experience in graduate school managing incoming expectations with sober, if foreign, realities.

Truth Values also pointed to a few lessons we computer scientists might heed. I would like to highlight a stark contrast between the reality of computer science education and research and that of mathematics. The gender imbalance in computer science is awful, at both the undergraduate and graduate levels. This makes it hard to tease out exactly why this imbalance exists. The story with mathematics education is different. And that just might provide computing educators some insight.

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MOOCS & The Virtues of an Educator (not a Programmer)

Like many, my complaint with the MOOC isn’t the MOOC itself, but the surrounding hype. My complaint is simpler than the eminent devaluation of teaching in terms of personal and public investment thanks to the MOOC marketing (as Bogost has argued and others have provided evidence). My primary complaint with the hype is the hubris.

The MOOC hype grossly underestimates the computational difficulty of teaching.

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Computing is a Liberal Art

“… the computer is being used to program the child. In my vision,
the child programs the computer and in doing so, both acquires a
sense of mastery over a piece of the most modern and powerful
technology and establishes an intimate contact with some of the
deepest ideas from science, from mathematics, and from the art of
intellectual model building”

— Seymour Papert in “Mindstorms: Children, Computers, And Powerful Ideas”

Computing is a Liberal Art is my personal blog on computing, robotics, liberal arts education, and various other things. Why? Students studying the liberal arts and sciences need lucrative, creative career opportunities. The tech industry needs a more diverse, creative work force. Moreover, it is imperative that all citizens have a role in shaping tomorrow’s technology, not just the technologists — assuring all citizens are programming rather than being programmed.

As computation plays a larger role in our professional, personal, and civic lives, thinking and communicating algorithmically has become as important as literacy in any other sense. And although being skilled at a particular computer application is often a prerequisite, it isn’t the whole story. As Papert (a mathematician, computer scientist, and student of Piaget) remarked concerning the potential of computing, “a revolution in ideas that is no more reducible to technologies than physics and molecular biology are reducible to the technological tools used in the laboratories or poetry to the printing press.” Being truly computationally literate means one can begin to think using the symbols and ideas of computation to solve problems or make art in new ways.

Some related takes on computing & the liberal arts: