Developing Skills Within a Degree Curriculum
Strong curriculum design involves the development of program- or degree-level learning outcomes that are addressed across a sequence of courses. These learning outcomes are concepts, skills, strategies, or ways of thinking that students need to be successful in their careers. When developing a curriculum or program with computation skills integrated throughout, it is important to keep in mind that there needs to be a consistency of tools across the courses, reinforcement throughout the curriculum, and a focus on computational skills that will improve job/graduate school readiness. Students benefit from making connections between courses, but they do not commonly make these connections on their own. Structuring a curriculum to make the connections clear and intentional helps the students transfer the skills between courses and build their confidence. Below are models from departments that are implementing computation into their curriculum.
What skills do you want students to have by the time they complete their degree? The challenge for every department or program is to develop a collective vision for these goals and how to implement them. One strategy for weaving these computational learning goals throughout a curriculum is by following the matrix approach to program design. The program matrix provides a visual representation of in which courses, how often, and how in-depth the learning goals are addressed in a curriculum.
Workshop participants identified the following examples of computational competencies that can be addressed using MATLAB:
Developing and Using Models Teaching Modeling with MATLAB »
Acquiring, Evaluating, and Analyzing Data Teaching Data Analysis with MATLAB »
Creating and Interpreting Visualizations Teaching Visualization with MATLAB »
Computer Skills Teaching Computational Skills with MATLAB »
Webinar: Integrating Computational
Thinking into your Curriculum
Departments can take different approaches to building computation and modeling skills into a degree curriculum. Below are examples of STEM departments that have taken steps to do this by developing skills matrices, creating computational learning goals, and embedding computation using MATLAB into a sequence of courses.
The Siena College Physics and Astronomy Department made computation and modeling central learning goals of their degree curriculum.
- Siena College Physics and Astronomy Mission and Learning Goals shows how the department has woven computation and modeling into the central aims of their program.
- Siena College Computational Requirements by Course shows a successful example of how to infuse an undergraduate Physics program with computational skills.
- Siena College Learning Goals illustrates how the department connects learning objectives and assessment.
The William & Mary Geology Department developed a curriculum matrix to incorporate quantitative skills throughout their curriculum.
- Coherent Curriculum (Acrobat (PDF) 111kB Oct25 16) document shows an approach to using learning goals to develop a curriculum matrix.
- Skills Matrix (Acrobat (PDF) 142kB Oct25 16) document is the template matrix for the William & Mary curriculum matrix.
In their mission, the University of St. Thomas Physics Department describes how they aim to provide students with "understanding and appreciation" of computational techniques and problem-solving skills. They work to achieve this mission by embedding computation throughout the curriculum.
- University of St. Thomas Physics Curriculum Development describes how the department works to achieve their curricular mission.
- University of St. Thomas Physics - Embedding Computation shows examples of courses incorporating computation using MATLAB.
- Webinar: Teaching Physics with MATLAB: Simulations and Experiments by Marie Lopez del Puerto. Includes examples of activities from the UST curriculum.
These essays highlight approaches to building students' computation and MATLAB skills across the curriculum.
- Teaching and Integrating Computation in Graduate Research by Michael Cardiff, Geosciences, University of Wisconsin-Madison
- Integration Enhances Learning Across the Curriculum by William Cluett, Chemical Engineering & Applied Chemistry, University of Toronto
- Using and Learning Matlab in Geomorphology of Rivers: Experiences with Advanced Students and Research by Andrew Darling, Geosciences, Colorado State University
- More Content and Less Time: Opportunities and Challenges for Interdisciplinary Marine Science Students by Andrew Fischer, Institute for Marine and Antarctic Studies, University of Tasmania
- The Skills Gap Between the Graduates we Produce and the Graduate Students we'd Like to Recruit (or, "How I Bit the Bullet and Tried to Teach Computing.") by Gareth Funning, Earth Sciences, University of California, Riverside
- Forming A Biomathematical Learning Alliance Across Traditional Academic Departments by Gregory D. Goins, Biology, North Carolina A & T State University
- Creating Core Course Undergraduate Research Pathways Via MATLAB Simbiology by Princess Imoukhuede, Bioengineering, University of Illinois at Urbana-Champaign
- Baby-Steps Bring More Computation (or at Least "Math") into Biology and More Biology into Math by Anne Walter, Biology, Saint Olaf College
- Advancing the Neuroscience Curricula, a Computational Thinking Approach by Daniel Zysman, Brain and Cognitive Sciences, Massachusetts Institute of Technology
- Join the Teaching Computation in the Sciences Using MATLAB community to discuss ideas and ask experts questions