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Why Teach Computational Thinking?

Pause for a moment and do this simple activity:

Computational What?

Open a web browser.
Choose a subject area that you will teach or are interested in (e.g., literature, biology, physics, dance, language, etc.)
Do a Google search of the phrase "computational ___" but replace the blanks with the subject area you chose (e.g., computational literature, computational biology, computational dance).
Click on the first few links and skim the articles or web pages that you find.
Repeat steps 2-4 for another subject area.

The above activity illustrates a simple fact: computational methods are beginning to permeate inquiry and problem-solving activities across many domains, even domains that have not been traditionally associated with computer science.

To prepare for a future in which these computational methods are increasingly relevant and commonplace, students need to understand some basics. We're not talking about typing, word processing, and Google searches. We're not even talking about wise use of social media and digital citizenship, though these are crucial subjects in their own right.

What we are talking about is Computational Thinking (CT). The goal of CT is succinctly described in the following educational standard (developed in 2011 by the International Society for Technology in Education [ISTE]):

"Students develop and employ strategies for understanding and solving problems in ways that leverage the power of technological methods to develop and test solutions."

Of course, the ISTE standards don't have any binding influence in education, but many countries are beginning to adopt computer science standards into their mandatory curriculum, and computational thinking figures strongly in those standards. Currently, the United states has not officially endorsed such standards, but it does appear to be moving in that direction.

One reason for many countries' adoption of computer science standards appears to be economical: Exposing students to CT at an early age may lead many who would not otherwise have done so to consider occupations in computers and information technology, a career group that, according to the United States Bureau of Labor Statistics is projected to grow faster than average over the next 10 years (13%) and pays more than double the median wage of all occupations.

But computational thinking it is not the exclusive property of computer scientists, and it shouldn't be considered simply as a way to get students into IT careers. Rather, CT is interdisciplinary, transferrable, and, many argue, foundational to 21st century success. Whether or not they end up being the ones behind the computer, professionals of the future will need to understand how to best communicate with these professionals, and how to think about and formulate problems in such a way that they can leverage the power of computers to help solve problems - regardless of what domain they eventually end up working in. Integrating CT into the classroom can also support cognition and performance in other core subjects. (For a more complete treatment on computational trends and benefits see the Integrating Computational Thinking chapter in this open textbook).

What is Computational Thinking?

So, what exactly is computational thinking? For a quick overview, please watch the video below:

Computational thinking (CT) includes skills, attitudes, and approaches that empower you to leverage the power of computers and computational methods to analyze problems and design and test solutions for many types of problems. As you complete the Understanding Computational Thinking badge, you'll learn more about attitudes and approaches of CT, as well as types of problems it is really good at solving. For now, just try to wrap your head around the basic skills commonly associated with CT. Think of this list of skills as a heuristic you can follow as you approach a problem suited for computational thinking.

Basic Skills of Computational Thinking (CT)

(compiled from Google's Computational Thinking for Educators Course and CAS Barefoot's guide to Computational Thinking)

Decomposition: Breaking down data, processes, or problems into smaller, manageable parts

Pattern Recognition: Observing patterns, trends, and regularities in data

Abstraction: Identifying the general principles that generate these patterns

Algorithm Design: Developing the step by step instructions for solving this and similar problems
Evaluation: Making judgement about the value of a finished product.

NOTE: As you complete this badge, pay special attention to the tutorials link at the top of the Badgr badge. It will guide you step-by-step through the process and provide additional information that is necessary for you to complete the assignment.

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