Initial Publication Date: October 30, 2017

Workshop Synthesis

In a final session of the 2016 workshop on teaching computation in the sciences using MATLAB, participants reflected on what they learned and what they will be bringing back to their colleagues. This page summarizes some of the common themes of that discussion.


Computational Skills Crosscut Disciplines

  • There is a synergistic relationship between science, math, computation and social science in developing the ability to solve real world problems - computational thinking is an essential piece of this
  • Learning outcomes for computation that cross disciplines include skills with models, data, visualization and computers
  • A conversation across disciplines and within programs with faculty who have different levels of interest and thinking in computation is important to defining the role of computation in the curriculum.

Teaching Approaches

  • There are several models for developing computational skills -- including in degree program requirements/outcomes, infusing in courses across the disciplines, developing computational skill courses
  • There are a rich suite of strategies and resources for both teaching data analysis and visualization and developing capacity for using models to solve problems
    • Participants of the 2016 Teaching Computation in the Sciences Using MATLAB workshop started a collection of relevant teaching activities
  • Learning to break down problems into computable parts and algorithmic thinking are as important as learning how to program
  • Helping students understand how to use error reports and to debug are common challenges
  • Using a single tool allows students to focus on skills and not tools. Multiple tools helps understand the underlying principles. Finding synergies between tools allows both of these things to happen. Knowing your tools eases tool choice. Choosing your approach is dependent on your context (e.g. undergraduate, graduate, discipline, department culture).

Affective Domain

  • Fundamental to successful learning is helping students know when to ask questions and be comfortable answering them. Addressing the affective domain can help faculty increase their effectiveness with helping students achieve comfort asking and answering questions.
  • Developing comfort and grit are important aspects of developing computational skill
  • Infusion in courses across the discipline requires motivating learning, development of basic skills, and giving programming experience. The (see Building Comfort in Courses page) shows models for how to scaffold skill development in a course. Motivating students is an important component of getting students to learn new material and how to use new tools.
  • Assessments are essential for both quality instruction and grading. Rubrics are a strong strategy for assessing student learning.

Building a Community of Practice Focused on Teaching Computation

  • Workshop participants share an enjoyment of computational thinking and think it is important for undergraduate curricula.
  • We found value in meeting with colleagues across the disciplines to discuss our shared interests.