Questions for reflection on computational thinking

Among other things, I am starting a group at work to discuss pedagogy and teaching of STEM subjects. At our meeting next week, we are discussing Shuchi Grover and Roy Pea’s survey article on computational thinking from 2013. I put together a list of reflection questions and prompts to get our conversation started. I’m pretty happy with the list, and thought I’d share it.

Without further adieu, my list of reflection prompts:

• Since Jeanette Wing’s 2006 introduction, the phrase “computational thinking” has resurrected the notion that within computer programming lies a universally applicable skill. Since then, politicians and other social leaders have jumped on the bandwagon of learning to code. Why has computational thinking become such a rallying cry for CS education? What are its implications? What are people looking for that they didn’t find in “programming”, “coding”, or “computer science”?
• Jeanette Wing defines computational thinking as the thought processes involved in formulating problems and their solutions so that the solutions are represented in a form that can be carried out by an information processing agent. There is disagreement on this definition. What should computational thinking mean? How is it different from programming? From computer science? How is it related to abstraction, decomposition, algorithmic thinking, mathematical thinking, logical thinking, computational modeling, or digital literacy?
• Computational thinking is described as a universal skill set that is applicable to everyone, and not just computer scientists. Is this fair? If the current generation of computational thinking curriculum looks a lot like computer science, how are we living up to that standard? If the skill set is so universal, where are the examples from before computer science existed as a topic of study?
• The skills needed to both use and create computational artifacts are changing rapidly. How does this impact the meaning of computational thinking? Suppose that, in a few decades, most computer software will be created by non-specialists using informal language and NLP; what does that mean for computational thinking?
• The authors compare computational thinking with computational literacy, which includes social concerns and computation as a medium for math, science, or art. What can we say about the relationship between the two? What, if anything, distinguishes these ideas?
• The elements of computational thinking are listed as abstraction, information processing, symbolic representations, algorithmic flow control, decomposition, procedural (iterative, recursive, and parallel) thinking, conditional logic, efficiency and performance, and debugging. How do these topics really fit together? How would we see them differently if computer science were not involved?
• There are hundreds of very different competing programming languages and tools vying for using in computing classes. How do we identify a universal body of knowledge in such an environment? What does this do to the possibility of transfer learning? Can we assess computational thinking in the absence of assumptions about languages and tools?
• The authors point out that Scratch, the most popular K-8 programming language, is missing procedural abstraction, even though abstraction is “indisputably” the cornerstone of computational thinking. What other important ideas are missing from common tools? What can we do about it?
• The authors claim that few efforts in computational thinking take into account socio-cultural and situated learning (i.e., how we learn through apprenticeships and relationships with others), distributed and embodied cognition (i.e., how cognitive processes take place across the whole body and through communication between multiple people), or activity, interaction, and discourse analyses (i.e., study of the way communication happens in the classroom). How would these perspectives change our view of computational thinking?