Workshop Synthesis

At the 2018 Teaching Computation in the Sciences Using MATLAB workshop, faculty participants gathered to share their strategies, challenges, and approaches to teaching computation. The workshop featured a backward design framework, where workshop sessions and discussions focused on the design and alignment of leaning goals, assessment, and teaching. Here, we present cross-disciplinary learning goals, assessment techniques, teaching strategies, and community resources discussed at the workshop. The synthesis is not meant to be an exhaustive treatment of these topics; rather, it is a summary of the key ideas from workshop presentations and discussions.


Cross-disciplinary computational learning goals

Participants at the workshop discussed shared computational learning goals (within disciplines and between them) at length. The consensus was that the fundamental computational skills and competencies faculty want students to gain are inherently interdisciplinary. Interdisciplinary computational learning goals discussed by faculty participants include:
  • Using computation as a tool for problem solving. Computation and computational tools (such as MATLAB) can be used to address complex and real-world problems. Quantitative or programming skills alone do not make student good problem-solvers.
  • Handling, manipulating, and visualizing data. Developing data skills - how to collect it, how to store it, how to process it, and how to recognize patterns in it - is critical for students in all STEM disciplines.
  • Building computational competencies. Developing a basic computational vocabulary and computational literacy provides students with a foundation that they build from as they advance through their academic and professional careers.
  • Developing a computational growth mindset. Building student self-efficacy and comfort with computation and computational tools helps to promote the continued learning and kills development. Patience, persistence, and and resiliency are key mindsets to becoming computationally fluent.
Participants detailed their activity- and course-level learning goals in submissions to the workshop teaching activity and course collections.

Techniques for teaching and assessing computational learning

Throughout the workshop, participants discussed their strategies for teaching and assessing student computational learning. Faculty presented a wide variety of effective techniques and tools that they use in their courses. Broadly, these strategies are shared across disciplines and can be used in courses in any field.
  • Example strategies and techniques
    • Mastery- and standards-based grading are techniques that focus on student learning goals, targets, or concepts. Rather than receiving letter grades, students are assessed on their mastery or understanding of the given concept. Students are allowed multiple attempts on assignments to show they are mastering the material. When applied effectively. These techniques can build students' growth mindset and reduce the fear of failure. Read more about mastery- and standards-based learning »
    • Collaborative assignments can be effective tools for enhancing and assessing student learning. Assessing collaborative work is often noted as a challenge when using cooperative learning, but using strategies such as intentionally structuring teams, having students write group contacts, conducting peer assessments, and requiring students to keep an 'activity log' can make it an effective approach. Read more about cooperative learning »
    • Low-stakes assignments allow students to build computational confidence and take "risks" while providing instructors with formative feedback. Faculty participants discussed many varieties of low-stakes assignments, including entry and exit "ticket" assignment, self-reflection and metacognitive tasks, regularly scheduled quizzes or other assessments, and open note examinations. Read more about building student self-efficacy »
  • Assessment tools
    • Rubrics help to set student expectations, provide guidelines for success, and increase the reliability of scoring.
    • MATLAB Grader is a tool for creating and automatically grading MATLAB code assignments in any learning environment.
    • Gradescope is an online rubric-based tool for rapid grading grading paper or code assignments.
Participants wrote about their successes and challenges when assessing student learning in their pre-workshop essays.

An exemplary collection of teaching materials

At the 2018 workshop, a group of faculty members met to review the growing collection of teaching activities submitted by workshop participants. The reviewers completed a thorough and systematic review of the activity collection. Activities that received high ratings during the review were marked as exemplary and added to a collection of exemplary activities. The review process and exemplary collection serve to

  • provide a resource for other faculty-educators seeking strong activity examples and ideas,
  • highlight strong teaching practices,
  • and allow activity authors to receive academic credit for their contributions.

A growing community of faculty teaching computation with MATLAB

Among the many things that participants said they would take back to their courses, departments, and institutions, a common thread was the community of faculty with which to collaborate and share. Participants expressed an interest in building interest in teaching computation and MATLAB among faculty and students in their departments. Many of the challenges individual faculty face in teaching computation are shared. These common challenges can be more effectively addressed through the work and collaboration of the community. There are a broad array of community-built and community-oriented resources for teaching computation and using MATLAB: