I’ll admit it: Coding scares me. I’ve never been great shakes at math. Even rooming with a computer science major throughout undergrad didn’t help demystify it for me. She made it as simple as it can but it still always sounded like far too much to keep track of for me. I went to high school recently enough that basic website coding was part of the curriculum, but it was always hours of frustration for a mediocre product for me. All of this to say that I was fairly intimidated by this week’s topic!

The basic premise of computational thinking as problem-solving and logic exercises is something I was already familiar with, though maybe not in those terms. Snelling’s (2018) story about Claudia Haines become “Robot Claudia” and doing the monkey exercise reminds me of a similar activity my teachers would do in elementary school. They would assign us a process such as “make a peanut butter and jelly sandwich” and it was our job to describe the steps of that process in exacting detail and clarity. Our teacher would do exactly and only what we wrote, usually leading to messy consequences. This was framed as building literacy, but it now strikes me that what it was building was algorithmic thinking. In coding, every input must be precise, and the computer cannot do more than exactly what you tell it to.

Additionally, my undergraduate minor in linguistics comes in handy in the classroom. In Special Education, I tend to get a lot of students who prefer the logic of math over what they perceive as the ambiguity of writing. In linguistics, we map sentences in the same way that a computer scientist codes: in small chunks of data. I’ve found that this analogy helps students who see writing as ‘anything goes’–by pointing out to them that a sentence is a kind of equation that requires pieces of data such as ‘verb + subject’. Sheldon (2017) calls this “decomposition”, which is kind of amusing when you think about applying it to a literal composition paper. There is of course a reason that coding is referred to as ‘language’. All language breaks down to algorithms composed of data, input (audible phonemes) stored (heard) then processed by the brain to create output (meaning).

After all of that analogy and thought, my own output is likely to be disappointing. I spent exactly an hour on this, which is about par the course for how low my skills in coding are. At least this time, the process was infinitely less frustrating due to the block coding rather than writing code from scratch. Enjoy the silly fruits of my labor!

https://studio.code.org/projects/dance/lBLP-qkJUO-t6XVpETlcf_4Ged6MUdCTg0AYEjxDs3Y

Sheldon, E. (2017, March 30). Computational thinking across the curriculum. Edutopia. https://www.edutopia.org/blog/computational-thinking-across-the-curriculum-eli-sheldon

Snelling, J. (2018, April 3). Don’t stress about coding: Focus shifts to teaching problem solving not computer skills. School Library Journal. https://www.slj.com/?detailStory=dont-stress-coding-focus-shifts-teaching-problem-solving-not-computer-skills