Tuesday, November 10, 2015

Computation Thinking

When I was in third grade, would always love to play Reversi, a simple board game where you try to capture opponents pieces by surrounding them with yours (kind of a simplified version of Go). For whatever reason, I distinctly remember the side of the game box advertising it as "minutes to learn, years to master". Using this sentence as a segue, I would like to relate this to the concept of "low floor, high ceiling" found in the Grover and Pea article. I've taken my fair share of CS classes, even going so far as to tinker around with developing an app with one of my friends. It really is pretty easy to get the ball rolling with a "hello world" program, but programming offers so much growth all at the fingertips of the user (my friend went on to study CSE at MIT, so there's that). I think this sort of topic is very helpful, especially when considering the wide range of aptitude found in a typical classroom. This quality could also be extended to other domains. In chemistry, for example, how can I as a teacher implement strategies that allow for this sort of opportunity for growth? 

Both Grover and Pea as well as Sengupta et al. discuss the benefits of thinking like a programmer when approaching a program. I've heard that philosophy and CS are strangely intertwined. While counter-intuitive, it does make sense; both philosophy and CS require approaching a problem or idea with an open mind, logical, sequential steps, and a comprehensive organizational hierarchy. However, this thought process could be applied to almost any problem, not just CS or philosophy. In lab, for example, a problem or issue might arise an answer for which hasn't been found in literature. Solving this problem requires thinking from a variety of angles. A good chemist will not think just of one variable, but many, and how changing a single step in a procedure might affect the outcome. Or, sort of the reverse, a good scientist should be able to look at a problem holistically, and synthesize all relevant data into a plan or path of investigation.

1 comment:

  1. Your comment about the similarities between philosophy and computer science in approaching problems is interesting. It makes me think maybe computational thinking is not the only way for students to achieve problem solving practices. The ability to identify a problem, devise ways to solve it, and then defend it convincingly doesn't lend itself exclusively to science.