The classic definition of computational thinking was created in 2006 by computer scientist Jeanette Wing. According to Wing, computational thinking (CT) involves solving problems, designing systems, and understanding human behavior, by drawing on the concepts fundamental to computer science (Wing, 2006). CT isn’t just for computer scientists, but a fundamental skill for everyone. It is a way humans solve problems and it is not trying to get humans to think like computers (Wing, 2006). We all practice CT in some capacity even without computers. Packing your bag with things you need for your trip or retracing your steps to find an item you lost are problem-solving strategies relative to computational thinking. Wing’s hope is that CT competencies become more widely recognized and spread to other disciplines (Wing, 2006).
In 2012 , ISTE and Computer Science Teachers Association developed an operational definition of CT to help K-12 teachers introduce it in their classrooms. The timing of generating these standards is consistent with growing employment opportunities in the United States. According to the National Science Foundation, more than 600,000 high-paying technology jobs are open across the US, and as of 2018 more than 51% of all STEM jobs will be in computer science-related fields (Lindstrom et al., 2019). Therefore, teaching CT as a critical problem-solving process will better equip our students to be prepared for the job market that they will be entering. Not all our students will enter a computer-science related field, but CT is universally important in solving and understanding complex problems.
ISTE recognizes that bringing CT to K-12 classrooms faces challenges of introducing it to the curriculum to getting teachers fully onboard. Many teachers don’t yet know what computational thinking is and get hung up on the definition (Fingal, 2018). I can recall the day I attended an ISTE training put on by our district and thinking how irrelevant CT was for social studies. I didn’t realize at the time that I had been teaching facets of CT, but I didn’t have the knowledge and understanding to communicate these processes to students.
Fast forward to my role as a digital education coach, I wish to help educators understand that they are already engaging their students in CT, find new ways to integrate CT into their existing curriculum, and foster a better understanding of the characteristics of CT so that it is explicit for our students and to empower them transferring these processes to other problems. More specifically, I want to investigate how CT is integrated into social studies to better support some of the teachers I work with.
My question – What is computational thinking and how can it be integrated into social studies?
ISTE 5 Computational Thinker – Students develop and employ strategies for understanding and solving problems in ways that leverage the power of technological methods to develop and test solutions.
Computational thinking forms links between computing and the real world including a set of problem-solving processes that builds on the power and limits of computing. The focus is on thinking skills or processes and the four most commonly cited of CT are decomposition, pattern recognition, abstraction, and algorithmic thinking. Many social studies educators may realize that they are already fostering these thinking skills in their curriculum and with their students. The following includes a breakdown of CT thinking skills as well as examples of how they could be integrated into social studies curriculum:
Decomposition is the breaking down complex problems into smaller parts or tasks. Decomposition is great for breaking down essential questions or historical topics/processes to better analyze and understand. Students can break problems down into smaller tasks to work at one at a time to limit the chance of being overwhelmed (Güven & Gulbahar, 2020). This is a common skill in social studies as historical events and periods are broken down into parts such as causes and effects or varying perspectives. For example, while investigating The Great Depression, students could look into the main causes for the stock market crash and how the results affected the country, its economics, and people. They could then analyze the different parts to infer why the period is known as “the Great Depression” (Güven & Gulbahar, 2020). Similarly, there is a common social media trend where large topics are broken down into simpler and more easily digestible parts. These posts often begin with: “so you want to talk about _____”. This is a great example of decomposition that can be engaging for students to leverage digital tools to communicate complex information in a way that is appropriate for a target audience. It is common for educators as the drivers of this thinking often categorizing or breaking up topics for students. Instead, educators should consider empowering students to participate and collaborate in this process as much as it is appropriate.
Pattern recognition concentrates on finding similarities and differences in systems that can also be used to make predictions. Like decomposition, pattern recognition is common and easily integrated into social studies curriculum. We can study history, for example, to identify patterns to make better decisions in the future. Investigating change over time or compare/contrast already lends itself to pattern recognition that can then be used to make predictions or arrive at conclusions. For example, students can investigate maps of settlements or population distribution, investigate the rise and fall of civilizations, or examine primary source data to study patterns of voting rights in a nation (Hammond et al., 2019).
Abstraction can be described as reducing detail to make a problem or analysis more understandable. Another way to think about CT abstraction is the filtering information to glean the most relevant information. In other words, can I remove details to make it easier to see patterns or connections? For example, students discern the most important details shared in articles they research to write informatively about the subject. In civics, abstraction can be used to filter data to be analyzed then generate conclusions. Build in time for students to continually ask questions as this will help them consider new ways to analyze data and patterns.
Algorithmic thinking/design is developing processes through logical, precise, and repeatable steps (Güven & Gulbahar, 2020). It would be helpful to preface this CT with some basic knowledge of coding including vocabulary like sequence, selection, and repetition, but it isn’t critical. Students can develop their own algorithms to teach processes. Students could be empowered to research and create algorithms for how a bill becomes a law or the process of gentrification. Generally, students may use algorithmic thinking to demonstrate their understanding of major ideas, eras, themes developments, and turning points throughout history (Güven & Gulbahar, 2020). Simulation games like Mincraft or the oregon trail really exemplify this particular CT skill when the user is creating or playing through a narrative.
To be clear, computer science is an academic discipline involving the study of computation and application using computers while CT is a way we go about tackling problems using big picture processes (2016). CT helps increase student confidence with ambiguous, complex, or open-ended problems. Many social studies educators are naturally teaching CT though it may not be explicit. There is crossover between common historical thinking skills and CT. It is then critical to teach students the vocabulary associated with CT to support a deeper understanding of the thinking skills as well as increase ability in transferring those skills to other problems. In addition, providing space for students to choose, evaluate, and discuss their CT process can support higher level critical thinking. Encourage students to generate questions. Questions ignite the thinking process and also redirect the thinking process. Students may start with a driving question that evolves into other questions that affords a much deeper learning experience. New questions also may determine different ways to manipulate data or look for alternative patterns.
Fingal, J. (2018, November 27). Teaching computational thinking more important than defining it. ISTE. https://www.iste.org/explore/Computational-Thinking/Teaching-computational-thinking-more-important-than-defining-it.
Güven, I., & Gulbahar, Y. (2020). Integrating Computational Thinking into Social Studies. The Social Studies, 111(5), 234–248. https://doi.org/10.1080/00377996.2020.1749017
Hammond, T. C., Oltman, J., & Salter, S. (2019). Using Computational Thinking to Explore the Past, Present, and future. Social Education, 83(2), 118–122. https://www.socialstudies.org/social-education/83/2/using-computational-thinking-explore-past-present-and-future#:~:text=Using%20Computational%20Thinking%20to%20Explore%20the%20Past%2C%20Present%2C%20and%20Future,-Social%20Education&text=The%20incorporation%20of%20elements%20of,for%20analyzing%20discipline%2Dspecific%20data
Lindstrom, D. L., Schmidt-Crawford, D. A., & Thompson, A. D. (2019). Computational Thinking in Content Areas and Feminine Craft. Journal of Digital Learning in Teacher Education, 35(3), 126–127. https://doi.org/10.1080/21532974.2019.1622917
What is computational thinking? (2016). https://www.youtube.com/watch?v=GJKzkVZcozc&feature=youtu.be.
Wing, J. M. (2006). Computational Thinking. Communications of the ACM, 49(3), 33–35. http://www.cs.cmu.edu/afs/cs/usr/wing/www/publications/Wing06.pdf.