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Building Bridges With Computational Thinking

Don’t you love those moments when students make connections between their world outside of school to the science content you are teaching? One of my favorite examples of such a connection happened during a physics lesson. I was using a diagram to introduce the concept of torque when one of my students said that he had seen the same idea that weekend. He went on to say that he had been changing a tire with his uncle using a lug nut wrench and they had needed to extend the length of the wrench using a metal pipe to get the lug nut to come loose. He had made a connection (i.e., built a metaphorical bridge) between his experience with his uncle and the content in our class. This moment was satisfying for us both and left me wanting to replicate the experience again and again. So, how do we support students in building those bridges between the “real world” and school? Computational thinking might be a route to consider.

Definitions of Computational Thinking Practices

  • Decomposition: Breaking down into components (K12 Computer Science Framework, n.d.)
  • Pattern Recognition: Finding similarities between components (Krauss and Prottsman 2017)
  • Abstraction: The process of reducing complexity by focusing on the main idea. By hiding details irrelevant to the question at hand and bringing together related and useful details, abstraction reduces complexity and allows one to focus on the problem (K12 Computer Science Framework n.d.)
  • Algorithm Development: A step-by-step process to complete a task (K12 Computer Science Framework n.d.)

Computational thinking

Jeanette Wing, in her seminal 2006 paper, defined computational thinking as “solving problems, designing systems, and understanding human behavior, by drawing on the concepts fundamental to computer science” (p. 33). Wing was emphatic that computational thinking skills be applicable to everyone—a belief that is echoed by the inclusion of computational thinking as a science and engineering practice in the Next Generation Science Standards (Appendix F; NGSS Lead States 2013) and the development of an entire set of competencies by the International Society of Technology in Education (see Resources).

Computational thinking is often described as an approach to problem solving (e.g., Lee, Martin, and Apone 2014), with applicability across content areas (International Society for Technology Education n.d). Within science specifically, computational thinking can be integrated with modeling and simulations (Adler and Kim 2018; Lee, Martin, and Apone 2014) along with automated processes for data collection and data mining (Sneider et al. 2014). Computational thinking, however, does not necessarily need to only exist in technologically rich (“plugged”) environments. We use computational thinking practices often in our daily lives to problem solve and troubleshoot (Resnick 2017). It is this natural instinct to use computational thinking that can provide the bridge between students’ “real lives” and science classrooms.

Starting with ourselves

To begin building bridges with our students, we should start by identifying how we already use computational thinking in our own lives. Examples of computational thinking practices, such as decomposition, pattern recognition, abstraction, and algorithm development (see sidebar for definitions), can be found in a number of daily tasks. Getting from home to work in the morning, for example, can be a complex problem to solve if we are using a series of public transit options (see Figure 1; see sidebar for commuting examples).

FIGURE 1:
Public transit map

Public transit map

Once we’ve started to see examples of these computational thinking practices in our daily lives, we can begin identifying the practices in our teaching lives. The computational thinking process to design a solution for our daily commute is similar to the process we use when setting up classrooms for a lab or experiment. For example, we use abstraction when considering which materials to include for students and which to leave out. We use algorithm development to create procedures, and we use pattern recognition when considering student proficiency to determine seating charts and groups.

Making the implicit explicit

With an understanding of how we use computational thinking ourselves as individuals and educators, we can begin to support students with identifying how they already use computational thinking. For example, we can ask students to identify how they approach their commutes to school in the morning, making the computational thinking practices that are currently implicit more explicit as they describe their thinking processes. A similar process can be used when asking students to describe how they use recipes when cooking (Erwig 2017) or how they pack their backpacks when preparing for school (Yadav and Caeli 2019).

Identifying how students already use computational thinking processes allows us to build bridges between how students think outside of school and how they think in our science classes (Gay, 2018). Students are able to see just how much they already know and how their prior knowledge can be used when approaching their learning in science. Such teaching is culturally responsive, as we are seeking to use the knowledge and skills that students have developed outside of school to teach them about new content and practices (Hammond 2015).

Computational Thinking Practices: Commuting Example

  • Decomposition: We might use decomposition to break the larger journey into smaller trips, such as getting from the house to the subway, riding the subway to a stop closer to work, and finally walking from the subway station to work.
  • Pattern Recognition: We could look for patterns in our smaller journeys to gather new insights, such as the duration of each trip and which subway lines run close to work.
  • Abstraction: Given that there are a number of options presented on our map, we might use abstraction to remove the unnecessary routes, including the subway lines that are not in our area.
  • Algorithm Development: Ultimately, we would be able to create a precise algorithm for how to get from home to work. Our commute algorithm could be repurposed when we head home in the evening.

Getting to “plugged”

Once we have been able to support students in seeing how computational thinking is used in both their daily lives and at school, we can begin to consider the practices as related to computing with machines. Environments created using Scratch (see Resources) can help students leverage their computational thinking skills to build programs that use algorithms designed for computers to implement. Both plugged and unplugged approaches to computational thinking are necessary and important (Yadav and Caeli 2019), and the process of starting with unplugged examples allows educators to build bridges and leverage students’ cultural knowledge and experiences.

Conclusion

Technology has become a central component of how we learn, work, and play. As science educators, we have the responsibility to bring technology into our classrooms while helping students recognize the power of human thinking in developing and using technology. We have the opportunity to help students recognize and leverage the kinds of computational thinking they already do in their “real lives” as they learn science in school. The process begins with making the connections ourselves, and then considering how we can do the same with students.

How else are you integrating CS into your middle school science classroom? Do you have a great resource or experience to share? Let me know via email!

Raja Ridgway (rridgway@relay.edu) is the director of computer science education at Relay Graduate School of Education in Denver, Colorado.

References

Adler R.F., and Kim H.. 2018. Enhancing future K–8 teachers’ computational thinking skills through modeling and simulations. Education & Information Technologies 23 (4): 1501–1514. doi: 10.1007/s10639-017-9675-1

Erwig M. 2017. Once upon an algorithm: How stories explain computing. Cambridge, MA: MIT Press.

Hammond Z. 2015. Culturally responsive teaching and the brain: Promoting authentic engagement and rigor among culturally and linguistically diverse students. Thousand Oaks, CA: SAGE Publications.

International Society for Technology Education. n.d. Computational thinking competencies.

K12 Computer Science Framework. n.d. Glossary.

Krauss J., and Prottsman K.. 2017. Computational thinking and coding for every student: The teacher’s getting-started guide. Thousand Oaks, CA: SAGE Publications.

Lee I., Martin F., and Apone K.. 2014. Integrating computational thinking across the K–8 curriculum. ACM Inroads 5 (4): 64–71.

NGSS Lead States. 2013. Next Generation Science Standards: For states, by states. Washington, DC: National Academies Press.

Resnick M. 2017. Lifelong kindergarten: Cultivating creativity through projects, passion, peers, and play. Cambridge, MA: MIT Press.

Sneider C., Stephenson C., Schafer B., and Flick L.. 2014. Exploring the science framework and NGSS: Computational thinking in the science classroom. Science Scope 38 (3): 10–15.

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

Yadav A., and Caeli N.E.. 2019. Unplugged approaches to computational thinking: A historical perspective. Tech Trends 1–8.

Resources

ISTE Computational Thinking Competencies

Scratch

Topics

Computer Science Interdisciplinary

Levels

Middle School

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