Article Summary: Tedre & Denning (2016) The Long Quest for Computational Thinking

Motivation: To present the history of computational thinking so that researchers do not repeat the mistakes made in the past or resolve problems. To identify potential threats to widespread implementation of CT in K-12 education.

Computational Thinking (CT): Computational thinking (CT) has a long past dating back to nearly the beginning of the computing field. Therefore, the definition of CT has evolved along with computing in general. The current conception of CT is a set of skills related to computing but useful beyond computing. This conception originated from Wing’s (2006) paper that argued CT should be as fundamental to education at reading, writing, and arithmetic. While many people agree with her argument, few agree on what should be included in that set of skills. Some aspects of CT that many (but not all) agree should be included in a generic CT framework are problem decomposition and formulation, data collection and organization, data representation and analysis, abstraction, algorithmic thinking, evaluation of efficiency and correctness (debugging), generalization, and pattern recognition. If there is one thing that everyone can agree upon, it’s that CT is not just coding. Coding and programming are some of the tools that can be used to solve problems that require CT skills, not CT itself.

The Utility of CT Beyond Computing: CT has obvious overlaps with other academic domains, especially engineering, science, and design. Problem decomposition and formulation are as integral to engineering as computer science, just as data collection and analysis are as integral to science as computing. Given these overlaps, many CT proponents argue for development of CT as general-purpose thinking tools, much like people used to argue for teaching Latin to develop general cognitive skills. Much like learning Latin, however, little evidence supports that CT skills spontaneously transfer to new contexts – a point well made by Guzdial (2015). Of course, just because CT does not spontaneously transfer to new contexts doesn’t mean that students can’t be guided to transfer their skills to other domains. The article provides many examples of CT implemented correctly to improve learning in K-12 education.

Risks to the Effective Implementation of CT: 

  • Lack of ambition – trying to implement a watered down version of CT instead of a authentically rigorous version of CT
  • Dogmatism – pretending that CT is the best or most important skill to teach, regardless of context
  • Knowing versus doing – telling students about CT skills rather than letting them practice CT skills
  • Exaggerated claims – over-promising the benefits of teaching CT, like that it will spontaneously transfer to other domains
  • Narrow views of computing – teaching coding instead of teaching CT
  • Overemphasis on formulation – defining CT as formulating problems so that a computer can solve it, which is too narrow
  • Losing sight of computational models – not using and updating the computational models that represent systems

Why this is important: We must learn and remember the history of CT so that we don’t repeat the mistakes or successes of the past. This paper makes the path to the current version of CT clear, even though it has been winding (through various disciplines) and overgrown (with different terms). The paper also charts the obstacles ahead so that we best overcome them. Perhaps most importantly, though, it reaffirms that integration of computational thinking throughout K-12 education is worthwhile and valuable for our education system, despite the challenges of the past and future.

Tedre, M., & Denning, P. J. (2016). The long quest for computational thinking. Koli Calling 2016, 120-129. doi: 10.1145/2999541.2999542

Wing, J. M. (2006) Computational thinking. Communications of the ACM, 49(3), 33–35.

Guzdial, M. (2015). Learner-Centered Design of Computing Education: Research on Computing for Everyone. Synthesis Lectures on Human-Centered Informatics. San Rafael, CA, USA: Morgan & Claypool, 2015.

For more information about the article summary series or more article summary posts, visit the article summary series introduction.

One thought on “Article Summary: Tedre & Denning (2016) The Long Quest for Computational Thinking

  1. Pingback: Article Summary: Series Introduction | Lauren Margulieux

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