This is part one of a two-part series investigating computational thinking in Biology.
What is computational thinking?
Why should biology students learn computational thinking?
How to engage students in computational thinking?
In this blog, I present a way to think about these questions.
As biology educators, we want our students to learn the art, the craft, and the science of biology as modern-day biologists practice it. The practice of biology is changing rapidly and one of the big contributors to the change is computational thinking. And this is not just for specialized fields like bioinformatics or computational biology but applies to the field of biology (and other sciences) in general. Computational thinking has the power to change how one goes about solving problems and designing solutions.
What is computational thinking?
Computational thinking has several definitions. To talk about computational thinking in biology, I draw upon one particular way of defining it:
Computational thinking in biology is how biologists think when they use computational tools and methods to research a phenomenon.
Computational thinking is helping us understand various phenomena related to the COVID-19 pandemic that all of us are experiencing now. From sequencing the viral genome to designing the vaccines involved extensive use of computational tools and a different kind of thinking to use those tools for solving problems.
In a recent interview, Dr. Dimitri Korkin describes a virus as a nonliving intelligent machine. Dr. Korkin and his team used computational tools to construct 3D structures of viral proteins and their interactions with human proteins and made a structural genomics map of the coronavirus available to the public all over the world.
Research investigating the evolution of SARS-CoV-2 in an immunosuppressant patient provides another interesting example of computational thinking in biology. The biologists used computational modeling and data visualization software FigTree and an in-house algorithm to study the evolution of the virus inside a patient. This work could potentially help predict possible new varieties of viruses in the world.
In these examples, the biologists engaged in a different kind of thinking while using computational tools, methods, and representations to learn more about a phenomenon, solve problems, and design solutions. This kind of thinking involves, but not limited to, programming or coding. It is more about how a biologist thinks differently when employing computational tools and methods to investigate a biological phenomenon.
Why should biology students learn computational thinking?
When I took an elective course on computational methods in biology during my undergraduate studies, something fundamentally changed in the way I think about biological systems. Using computational tools to represent and study a phenomenon in a virtual world, like the evolution of the SARS-CoV-2 virus in our earlier example, made me realize the power of the computational medium to investigate these phenomena deeply. As biology educators, it is important for us to introduce this power to our biology students.
Computational thinking can help biology students engage in phenomena differently and more deeply.
Research in modern biology has fundamentally changed because of computational thinking. We cannot teach the disciplinary ideas in modern biology and contemporary research practices without teaching computational thinking. So, how can we integrate computational thinking in a biology classroom?
How to engage students in computational thinking?
Learning Scientists have researched and designed ways to integrate computational thinking and computational literacy in science classrooms. These researchers interviewed scientists, mathematicians, and educators to arrive at a definition of computational thinking for science and mathematics classrooms. They identified four categories of practices that can be integrated in curricula to engage students in computational thinking. These practices are about data, modeling, and simulation, computational problem solving, and systems thinking.
Engaging students in these computational thinking practices by modifying existing biology curricula can be an effective way to introduce computational thinking to biology students.
Let’s briefly look at one classroom example.
One way to engage students in these practices is to use a computational model that is designed as an experimental ‘model system’. We can design curricular activities using this model to engage students specifically in different computational thinking practices. I worked with Ms. Teresa Granito, a biology teacher at Evanston Township High School, to modify her curricular unit about the evolution of populations.
To teach natural selection Ms. Granito used an example developed by hhmi BioInteractive: The Making of the Fittest: Natural Selection and Adaptation. This example is about the evolution of the color of fur coats because of natural selection in a population of rock pocket mice in the desert of New Mexico. Ms. Granito and I co-designed a computational model of a population of rock pocket mice using software called NetLogo and an accompanying curricular unit integrated with computational thinking activities.
Think of this model as a computational model system that allows us to investigate the phenomenon of natural selection in the population of rock pocket mice. Students can use a computational model of mice population to investigate changes in a population due to natural selection and learn computational thinking. In the next part of the blog, I will discuss how the curricular unit engages students in computational thinking practices.
How can this kind of teaching be facilitated in the classroom? In the next part of this two-part series, I will discuss how a teacher can support students learning computational thinking.
Sugat is a doctoral candidate in the Learning Science program in the School of Education and Social Policies, Northwestern University. Before starting his Ph.D. program, Sugat was a computational biology researcher and a management professional. He is interested in designing curricula and pedagogical practices to deeply engage students in the construction and evaluation of knowledge. He has worked on designing and co-designing computational learning environments for high school science classrooms. Currently, he focuses on equity-centered co-design approaches for developing new curricula that leverage teachers’ and students’ context-specific funds of knowledge. He wants to extend this co-design approach to diverse local and global communities to study how technology can support such co-design efforts.
Acknowledgments:
I would like to thank the CT-STEM team, including the teachers, who have helped me develop my ideas about computational thinking and co-designing computational thinking integrated curricula. The co-designed curriculum mentioned in the blog has been developed through generous support from the National Science Foundation (grants DRL-1640201 and DRL-1842374). Any opinions, findings, or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the funding organizations.