TITLE: Computational Thinking without Programming AUTHOR: Eugene Wallingford DATE: February 17, 2009 9:31 AM DESC: ----- BODY: Last week, I read a paper on changes how statistics is taught. In the last few decades, more schools have begun to teach stats conceptually, so that the general university graduate might be able to reason effectively about events, variation, and conditions. This contrasts with the older style in which it was taught as a course for mathematicians, with the focus on formulas and mastery of underlying theorems. The authors said that the new style emphasized statistical thinking, rather than traditional statistics. For some reason, this brought to mind the buzz around "computational thinking" in the CS world. I have to be honest: I don't know exactly what people mean when they talk about computational thinking. I think the idea is similar to what the stats guys are talking about: using the ideas and methods discovered in computer science to reason about processes in the real world. I certainly agree that most every college graduate could benefit from this, and that popularizing these notions might do wonders for helping students to understand why CS is important and worth considering as a major and career. But when I look at the work that passes under the CT banner, I have a hard time distinguishing computational thinking from what I would call a publicly-accessible view of computer science. Maybe that's all it is: an attempt to offer a coherent view of CS for the general public, in a way that all could begin to think computationally. The one thing that stands out in all the papers and presentations about CT I've seen is this: no programming. Perhaps the motivation for leaving programming out of the picture is that people find it scary and hard, so omitting it makes for a more palatable public view. Perhaps some people think that programming isn't an essential part of computational thinking. If it's the former, I'm willing to cut them some slack. If it's the latter, I disagree. But that's not surprising to readers here. While thinking on this, I came across this analogy: computational thinking with no programming is like statistical thinking without any mathematics. That seems wrong. We may well want stats courses aimed at the general populace to emphasize application and judgment, but I don't think we want students to see statistics devoid of any calculation. When we reason about means and variance, we should probably have some idea how these terms are grounded in arithmetic that people understand and can do. When I tried my analogy out on a colleague, he balked. We don't need much math to reason effectively in a "statistical" way, he said; that was precisely the problem we had before. Is he overreacting? How well can people understand the ideas of mean and standard deviation without knowing how to compute them? How little math can they know and still reason effectively? He offered as an example the idea of a square root. We can understand what a square root and what it means without knowing how to calculate one by hand. Nearly every calculator has a button for the square root, and most students' calculators these days have buttons for the mean -- and maybe the variance; I'll have to look at my high school daughter's low-end model to see. For the most part, my colleague feels similarly about programming for everyone. His concern with CT is not eliminating programming but what would be taught in lieu of programming. Many of the articles we have seen on CT seem to want to replace programming with definitions and abstractions that are used by professional computer scientists. The effect is to replace teaching a programming language with teaching a relatively formal "computational thinking" language. In his mind, we should replace programming with computational skills and ideas that are useful for people involved in everyday tasks. He fears that, if we teach CT as if the audience is a group of budding computer scientists, we will make the same mistake that mathematics often has: teaching for the specialists and finding out that "everyone else is rotten at it". The stats teaching paper I read last week says all the right things. I should look at one of the newer textbooks to see how well they carry it out, and how much of the old math skills they still teach and require. I'm still left thinking about how well people can think computationally without learning at least a little programming. To the extent that we can eliminate programming, how much of what is left is unique to computer science? How far can we take the distinction between formula and algorithm without seeing some code? And what is lost when we do? There is something awfully powerful about watching a program in action, and being able to talk about and see the difference between dynamic behavior and static description. -----