Initial Publication Date: January 17, 2018

Computational Thinking

Content on this page was originally created for On the Cutting Edge: Teaching with Data, Simulations, and Models and is expanded here. Other content is derived from participant presentations, discussions, and breakout groups at the Teaching Computation with MATLAB workshops and benefitted from the editing of Risa Madoff, University of North Dakota.

Computational thinking is the thought processes associated with examining a problem and formulating a solution that can be solved by a person or machine. More and more, scientists are turning to machines and programs to tackle complex problems and large datasets, requiring strong computation and programming skills.

Students who learn computational thinking and the skills to take advantage of programs such as MATLAB can take control of their data, explore models, and output results with a complete understanding of what happened. While most students do not become programmers, it is important for them to understand how computation and modeling contribute to scientific problem solving. With a little understanding and groundwork, a non-programmer can know enough to ask probing questions about programming output. Educators can ensure the development of these skills by exposing students to computation and programming through focused, scaffolded, and contextualized assignments that allow them to explore problems and the tools scientists use to solve them.

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Introducing Students to Computation

Other approaches discussed at the workshop included:

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Producing simple plots

Illustrating that the program produces things they are familiar with

Exploring the anatomy of a program - what is it?

Assigning variables/using command line
Scripts, functions

Diagramming - coding with intention

Creating visual representations of finding a solution to a problem

Metacognitive opportunities

Reflection about a problem, such as calculations, correlations, spatial and temporal scales, dimensionality, conversion, functionality, and error
Written diaries recording and reflecting on code, syntax, and programming

Getting Started with MATLAB

During the 2016 and 2017 workshops on teaching computation with MATLAB, Paul Kassebaum (MathWorks) discussed the value of computational thinking and tools to get students started with MATLAB. He also discussed resources on the MathWorks website that can be useful for implementing computation using MATLAB in your courses.

Paul Kassebaum's Presentations:

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In his 2016 presentation, Teaching with MATLAB, Paul discussed ways to incorporate and improve the use MATLAB teaching. He covers course content, pedagogical methods, course structure, materials and implementation, and faculty development and support.

Download the presentation (PowerPoint 2007 (.pptx) 9.2MB Oct25 16)

Click to view

In his 2017 presentation, MATLAB: A computing playground for science students, Paul showed how MATLAB can be used as a "computing playground" where students are able to interactively build code, run simulations, solve problems numerically or analytically, extract, analyze, and visualize experimental data, model, and simulate phenomena to build intuition, express and simulate equations to test hypotheses, simulate analytical models to test predictions.

Download the presentation (PowerPoint 2007 (.pptx) 5.5MB Nov20 17)

MathWorks resources for teaching and learning:

MATLAB Academy and Onramp: Free interactive, self-paced online learning
MATLAB Central: MATLAB community for sharing code and expertise
MathWorks Blog ( This site may be offline. ) : Learn from the MATLAB experts
Computational Thinking Resources from MathWorks

Resources

Introductory MATLAB Activities

Using Autocorrelation and Cross-correlation to Explore Links Between River Discharge and Regional Climate, Peter Adams (University of Florida). Students learn to write an efficient MATLAB script to load data, conduct analyses, and plot the results, followed by "publishing" their code.
Curve Fitting Exercise in MATLAB, Wendy Thomas (University of Washington). Students program using MATLAB to compare the fit of several models to an experimental data set.
Volume of Oceans, and Sea-Level Variations, Charly Bank (University of Toronto). In addition to advancing their computing skills, they also need to consider the limitations of their model and are asked to compose a scientific paper.
Isostasy and crustal thickness, Audrey Huerta (Central Washington University). Students learn to manipulate and code algebraic equations.
Scalar diffusion, Nimish Pujara (University of California - Berkeley). This problem set provides the students the chance to cement their understanding of scalar diffusion in a quiescent fluid. It is a useful first assignment that covers the basics of the physics and computations.
Introduction to MATLAB for Geomorphology, Risa Madoff (University of North Dakota). MATLAB is used to expose beginning students to using computer programming and mathematical concepts to quantify hillslope elevation change over time.
Global Warming: A Zonal Energy Balance Model and Tracking Groundwater Pollution, Victor Padron (Normandale Community College). Students develop basic skills in programming and scientific computing by writing their own MATLAB code with graphical representation of the solutions.
Introduction to MATLAB for Oceanographic Data, Anna Pfeiffer-Herbert, Stockton University. Students explore scalar and vector time series and profile data commonly used in the field of Oceanography using data sets from publicly available sources or that they collected in local waters.