Computational Thinking
in Science and Math

Promoting computational thinking in high school science and math
to empower all students to participate in a computational future.
Northwestern University National Science Foundation Spencer Foundation

We are a team of scientists and educators at Northwestern University working to design and research computationally enhanced high school science and math curriculum. With support from the National Science Foundation and Spencer Foundation, our goal is to enrich science learning while developing foundational computational literacy skills for all students.

What do we mean by Computational Thinking?

Modern scientists, engineers, and mathematicians use an array of computational tools and practices in their everyday work. It is increasingly important for high school students not just to know how to use computers but to be computationally literate. To help guide the creation of CT activities, lessons, and assessments, we have developed a taxonomy of relevant computational practices organized into four strands.

Data Practices

Data lie at the heart of scientific and mathematical pursuits. They serve many purposes, take many forms, and play a variety of roles in the conduct of scientific inquiry. The nature of how data are collected, created, analyzed, and shared is rapidly changing primarily due to advancements in computational technologies. Our taxonomy includes practices related to analyzing, collecting, manipulating, and visualizing data.

Modeling and Simulation Practices

The ability to create, refine, and use models of phenomena is a central practice for scientists and mathematicians. Such models can include flowcharts, diagrams, equations, chemical formulae, computer simulations, and even physical models. By definition, models are simplifications of reality that foreground certain features of a phenomenon while approximating or ignoring other features. Computational models are increasingly prevalent scientific tools used to predict, validate, and understand a wide array of phenomena. Our taxonomy includes practices related to creating computational models, assessing their strengths and limitations, and using models as tools for learning.

Computational Problem Solving Practices

As with much of human endeavor, problem solving is central to scientific and mathematical inquiry. While problem solving can take many forms, we focus on problem solving practices that are especially effective for working with computational tools and derived from the field of computer science. This category is intended to capture computer science’s contribution to contemporary scientific and mathematical work and the importance for today’s students to develop this skillset.

Systems Thinking Practices

Many of the important problems that we face today are complex, involving multiple variables, numerous direct and indirect effects, and are comprised of many, interdependent parts. With the increasing role of data and computational models, the ability to think from a systems perspective is an important part of what it means to be scientifically literate. Our taxonomy includes practices related to thinking in levels, understanding relationships within a system, and reasoning about the system as a whole.

What We Offer

Lessons and Activities

Our CT lessons cover a range of science and math concepts and are designed to be embedded in existing coursework.


We have developed a taxonomy of CT practices in STEM fields to structure our curriculum and assessments.


Our CT assessments use realistic STEM scenarios to challenge students and measure their progress.

Professional Development

Interested in trying CT activities in your classroom? Check out our professional development workshops and resources.


Our ongoing research explores the design and impact of CT activities in science and math classrooms.

Administrator Tools

Check out our new web based tools for students, teachers, admins, and researchers.