You thought it was pretty cool, or pretty creepy, when Amazon could make recommendations for purchases based on what you'd just bought. Or enticed you by telling you what other people bought when they bought whatever you'd just added to your cart. Peer pressure in shopping. What do they know that I don't?
What's behind those two very straightforward examples? Big data. Lots and lots and LOTS of data. Floods of data. All being crunched and analyzed by algorithms, and then further crunched and analyzed by the people who decide will appeal most to your data profile. Cool, and creepy.
The more we know about big data and its ongoing influences, the better. That's true. In spite of, or perhaps because of how uncomfortable it might make some of us. However, the fact that big data is not going anywhere underscores even more the importance of computational thinking.
You can figure out the gist of computational thinking by breaking down its name: computational + thinking. Maybe it makes you think people are expecting kids to learn how to think like computers. Hmmm, not exactly because, after all, computers are being trained to think like humans. However, without getting too weird, yes, in some ways, computational thinking is streamlined thinking and may seem a little too concrete for some. I think, however, perspective has a lot to do with it.
There are some who believe that computational thinking is the skill of the 21st century, and it could be. The folks at Queen Mary, University of London define computational thinking this way:
. . . a collection of diverse skills to do with problem solving that result from studying the nature of computation. It includes some obviously important skills that most subjects help develop, like creativity, ability to explain, and team work. It also consists of some very specific problem solving skills such as the ability to think logically, algorithmically, and recursively. It is also about understanding people.Some time ago I was asked if I knew anyone who might help with a data analysis project. I took a deep breath because this sounded boring to me, but then I was told the project was to analyze data for what wasn't there. Say what? In other words, not looking for specific patterns and trends in the data, but looking for what wasn't in the data. I found that immensely interesting though I couldn't imagine being the one to do the work. Still, the question of what don't you see stayed with me.
But let's get back to computational thinking.
Perhaps it might also remind us how interconnected our learning can be. That what we learn in science or math can often contribute to what we are able to do in ELA and social studies.
My friend Lori Feldman is a special needs educator and she's been talking about computational thinking for several years now. This chart from a Google class on computational thinking shows the relationship of computational thinking and subject areas that are not computer science. I've no doubt we could come up with lots of examples for each of the concepts and many related in the same subject area. For example, analyzing a character could be "recognize and find patterns or trends." I've no doubt musicians, dancers, architects, graphic designers, poets, gardeners, and many others could find applications for each of these computational thinking concepts, too.
The point is computational thinking is not new and, most importantly, it's relevant in areas other than computer science.
Even more importantly, computational thinking is a skill we have to integrate in our teaching as teachers and one we have to help our students develop and refine.