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Why students need to learn computational thinking

quantumprogress

Physics & Maths Teacher, John Burk makes some excellent points about teaching in general and specifically the use of Computational Thinking in particular in his blog post “Why students must learn computational thinking, and possibly, how to teach it”:

For a start he argues we should be pushing the concept and attitude of ‘thinking computationally’ rather than the current focus on programming:

“I say think computationally, and not program for a reason.

Thinking computationally is about learning when to use a computer so solve a problem. It’s all about identifying when and where computers will be useful in helping to solve a problem.

Thinking algorithmically is about translating the problem into an algorithm that you use to direct a computer to solve the problem.

Programming is the more mundane task of turning the algorithm into a machine readable set of instructions for the computer to carry out.

My claim is that we spend far to much time teaching people to believe that the incredibly exciting field of computer science is all about programming, when in reality it is about thinking, and this turns off most students, and prevents them from ever seeing just how a computer could transform what they are able to with their interests.”

Burk also agrees with Conrad Wolfram’s assessment of the need for a revolution in Mathematics Education. Wolfram argues that with the use of computers today we should see Mathematics problem solving as essentially a four step process:

His steps are:

  1. Posing the right question  – of a real world problem/issue
  2. Real world -> math formulation  – that is, start the problem in terms of its mathematics components
  3. Computation – solve the mathematics models & formulas involved
  4. Verification – implement the solutions into the original real world situation to verify their ‘correctness’, effectiveness, etc.

Burk then argues that these steps really involve Computational Thinking:

“There’s a direct correlation between the steps of math and computer science:

    1. Posing the right question (computational thinking)
    2. Real world -> math formulation (algorithmic thinking)
    3. Computation (programming)
    4. Verification (testing)

And just like math, computer science, as traditionally taught, spends too much time on step 3, neglecting all the others.”

Burk also argues that the answer is not in changing the subject ‘Computer Science’ (or whatever other IT course offerings we might have) but:

“I don’t think we can solve this problem by changing the way we teach computer science—too few students take this subject in high school or college to make a real difference.

Instead, we need to change the way we teach other subjects to take advantage of computational and algorithmic thinking.

So this is the challenge I’ve taken on—getting my students to see how the computer is a tool useful for answering questions in physics that we could not otherwise answer, and as the key to bringing real problems into the classroom. How I’m doing that, I’ll have to save for a later post.

I can already tell you I’m seeing promising results—in the form of more “cools” and “neats” than I ever heard when I actually taught computer science, and students being genuinely disappointed today when I told them we wouldn’t be working on designing models in the computer.”

For more see his blog post: 

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