Six Research Takeaways to Help You Understand Computational Thinking
In academia, you can find research on almost anything — from the sociolinguistic uses of emojis to obscure foodways. (Sometimes the research is unusual enough to warrant a so-called Ig Nobel Prize.) In the field of education, or in many social sciences, takeaways can provide useful applications within the classroom, or even for learning as a discipline. This is true of research centered on computational thinking, a problem-solving framework with ties to computer science.
Computational thinking is often divided into different parts, or elements. For example, decomposition is a type of computational thinking process wherein data or problems are broken down into smaller, more manageable chunks. Likewise, pattern recognition is the process of looking for patterns to aid in pattern prediction or in spotting anomalies. Papers often focus on these elements and offer up new ways to incorporate them into the classroom environment.
One way to learn more about computational thinking and see how you might incorporate it into your teaching or learning efforts is to read through some of the research papers on the topic. For example, how did a team of researchers quantify computational thinking? Does a paper’s explanation of decomposition clarify a salient detail and help you brainstorm a new class activity?
Here are some takeaways on computational thinking based on global research.
1. Computational thinking involves creative thought, not just rote code by computers.
While coding is an excellent way to put computational thinking into practice, it’s by no means a standardized process. This blog has mentioned before that computational thinking isn’t just coding. Sometimes computational thinking is pigeonholed into being just code or just computer-based learning. That’s not the case! Computational thinking is a problem-solving framework, a lens through which to view new problems.
Jeannette M. Wing’s seminal article is a great read that offers a good introduction to computational thinking. Published in 2006, it’s an oldie but a goodie, and it acts as a foundation for today’s expanding definition. A lot of computational thinking research will reference this article.
2. That said, some researchers found that children who learned programming for the sake of developing computational thinking strengthened their perception of code and critical thinking being related.
Researchers Gary Ka-Wai Wong and Ho-Yin Cheung set up an experiment to gauge students’ perceptions of computer code and its value to different subjects. They found that one of the stronger connections forged was between code and critical thinking.
For people who have debugged code or tried to develop a program’s architecture, this may seem totally obvious. That said, these researchers’ findings could lend credence to teachers outside of STEM using programming to enhance their students’ critical thinking skills. After all, perception is often reality when it comes to self-efficacy; if a student programs, and programming involves critical thought, then surely they’re a critical thinker, even if they say that they’re “not left-brained.”
3. A one-size-fits-all assessment for computational thinking tasks is still a bit up in the air, but there are a few viable options.
A trio of researchers (Marcos Román-González, Jesús Moreno-León and Gregorio Robles) shared a conference paper that took a deep dive into computational thinking assessment. They shared options that all had strengths and weaknesses but were found nonetheless to be complementary in their divergent perspectives.
Interestingly, one assessment (a Bebras task) doesn’t require code at all. Rubrics can also be handy in assessing projects. For example, you could identify elements that you want students to demonstrate, making the grading process more transparent.
4. Computational thinking can take different forms in different classrooms.
In this info-packed article from 2011, Valerie Barr and Chris Stephenson share ways in which computational thinking can be embedded in K–12 classrooms. Given that the article is a little older and that the definition of computational thinking is evolving, not everything will line up with expectations. Even so, there are several useful tidbits to be found.
The best takeaway is a table featuring different tasks relating to each of the computational thinking processes. For example, what is a spellchecker but an algorithmic ELA device? While not every process aligns with every subject, examples are provided for all fields.
5. Computational thinking isn’t just a concern for students; it’s a concern for teachers as well.
This paper from 2018 by Peter Mozelius offers a useful, if generalized, framework for training the trainer with regards to programming. As mentioned, although computational thinking doesn’t require code, programming can be a handy classroom exercise. Certainly, some of the case studies shared on this blog have shown how students have used the Wolfram Language to further their studies.
The sample schedule Mozelius provides doesn’t rely on a specific programming language, which makes the schedule more flexible. As a side benefit, training educators to code ties in well with companion lessons on computational thinking, connecting computational thinking elements with their practical coding applications.
6. Computational thinking isn’t just learning general knowledge — it’s the extraction of novel thought from known ideas.
In the October 2018 issue of The Science Teacher, Drew Neilson and Todd Campbell share an example of how adding a computational thinking component to a science lesson allowed students to not only find the “right answer,” but to move beyond it. Students were able to flex their critical thinking skills. Instead of just accepting the experiment’s results, they broke things down even further to discover the reasons behind those results. As a bonus for science teachers, there’s a standards-ready lesson plan included.
In a way, computational thinking is an extension of the scientific method, or a rigorous expansion of the so-called Five Ws. Even creativity coaches stress how children’s persistence in asking “why” all the time can improve even adults’ creative practices. Creativity can easily be found in STEM endeavors, explored through tools like Mathematica.
The breadth of research on computational thinking expands far beyond these linked articles. For example, Shuchi Grover and Roy D. Pea’s chapter on computational thinking does a great job of sharing a clear definition of computational thinking, all while providing tech-light, real-world examples. From the opposite tack, looking into the components of computational thinking (such as algorithmic thought) leads to further research. It’s likely that, as cognition and AI studies lead to new insights, those conclusions will color our perceptions of learning and computational thinking as well.
If this is something that interests you, be sure to look up computational thinking or one of its elements in a research database to find even more papers. Some databases will even recommend further reading based on who cited whom. Above all, see what you can apply from your reading — there are many ways to apply computational thinking in the classroom.
About the blogger:
Jesika Brooks
Jesika Brooks is an editor and bookworm with a Master of Library and Information Science degree. She works in the field of higher education as an educational technology librarian, assisting with everything from setting up Learning Management Systems to teaching students how to use edtech tools. A lifelong learner herself, she has always been fascinated by the intersection of education and technology. She edits the Tech-Based Teaching blog (and always wants to hear from new voices!).
