A Brief Background to Computational Thinking
Computational thinking is not a new idea, but it is one that would benefit from having more information about how to successfully bring the concept into the K-12 environment.
Computational thinking may seem like a new concept, but it has a richer history than you may expect. If you’re trying to help integrate CT into your classroom, it may help to know the background, especially if you have to make the case for CT to your administration.
In the 1960s, Alan Perlis and Seymour Papert were envisioning how the world would be impacted by machine automation. Perlis concluded that programming should be integrated into liberal higher education. While at MIT in the 1980s, Papert worked directly to help begin bringing computational thinking into K-12 education. Together with other researchers, these scientists helped create a pedagogical foundation for CT, but it has not yet become required in K-12 curriculum in many countries.
Computational thinking need not take up extra time, since teaching it can be integrated into other subjects.
As Russell Foltz-Smith discussed in a Tech-Based Teaching post, CT’s relative absence in K-12 is due in part to the lack of time for educators to pack another thing onto already overloaded schedules. Yet CT need not take up extra time, since teaching it can be integrated into other subjects. Unfortunately, most of the research done on CT has been conducted in undergraduate environments, particularly in computer science. This makes it difficult to show how to integrate CT into the K-12 curriculum; this blog, Tech-Based Teaching, is one place to help demonstrate how CT can be taught in different ways, across all subjects, to K-12 students.
As CT became a more familiar concept in the 2000s, research into the K-12 environment has produced some good recommendations for how to introduce its concepts to new learners. In my last post, I talked about the importance of a tool having a low threshold and high ceiling. Low threshold means new users are able to quickly see results of their activity, while high ceiling means more advanced users are able to use the same tool to do more complicated tasks. This combination of low threshold and high ceiling helps learners not become overwhelmed or disinterested in the tool. In an earlier post in Tech-Based Teaching, Elena Ivanova discussed her students’ experiences with this concept.
In 2016, Stephen and Conrad Wolfram both wrote about computational thinking. Soon thereafter, the Wolfram Computational Thinking Initiative was launched. Today, the CTI helps connect educators, CT advocates, and students together to help spread computational thinking. Computational Thinking Adventures, like How Secure is My Bike Lock?, introduce students to computational thinking using the Wolfram Language. Students can then take this knowledge to use the language in other ways — or to create a better code for securing their bicycles.
Preventing disinterest is central to creating effective CT curriculum. It can be easy to overstep boundaries and break a child’s interest in learning CT. Therefore, it is very important to allow the student to lead the lessons by self-directing their learning and focusing on issues that they find intriguing. In a previous post, The Tech Monsters gave readers examples of how to keep learning fun, casual, and interesting.
Once students have mastered a skill, they should be able to use the CT they developed during this experience to answer questions in their other classwork.
In higher education, integrating computational thinking often means finding bridges between CT and disciplinary issues, such as in the digital humanities. In K-12, the focus turns to more personal, less formal interests, such as telling stories or completing class assignments. For example, Flor Serna wrote in Tech-Based Teaching about her experience with project-based learning, where students practiced CT by participating in robotics competitions.
So far in the blog, we have had several posts incorporate this idea of hands-on, student-engaging learning. Other authors, including Connor Glosser, showed how to bring unfamiliar material into the classroom and make complicated ideas exciting for all students.
Research has also shown that curricular tools for CT need to follow a clear process, where their lessons can transfer to other areas. Once students have mastered a skill, such as designing their own computer-based game, then they should be able to use the CT they developed during this experience to answer questions in their other classwork. Or, to use Steve Jobs’ words about his computational learning as an adolescent, students should be able to use programming experiences “to be a mirror of your thought processes. To learn how to think.”
Computational thinking is not a new idea, but it is one that would benefit from having more information about how to successfully bring the concept into the K-12 environment. If you have examples of teaching CT in your classroom, I hope you will consider contributing them to the growing body of knowledge about CT — perhaps here, in the Tech-Based Teaching blog.
About the blogger:
Alyson Gamble
Alyson Gamble is a doctoral student in Library and Information Science at Simmons College in Boston, MA. As the editor of Tech-Based Teaching, she enjoys helping give educators an opportunity to discuss technology in the classroom.
