Modeling and simulation have become widespread in engineering research and practice. This workshop will present methodological guidelines for deploying modeling and simulation in the materials science and engineering classroom. These activities can be incorporated into the curriculum to enhance learning of core disciplinary concepts and student capacity in computation.

The facilitators will present two curricular examples developed and deployed within Johns Hopkins University’s Department of Materials Science and Engineering. The first will be a programming project from a MATLAB programming class in which the simulation of spiral wave propagation in heart tissue was used to teach programming concepts. The second will be an example from a kinetics and phase transformations class in which the COMSOL software package was used to help teach concepts of diffusion.

Participants will develop a plan for implementing a modeling and simulation-based exercise in the context of their own classrooms using locally available computational environments. Participants are expected to bring their own laptops for this exercise. Access to COMSOL, MATLAB or other specific software is not required.

The facilitators have collaborated on engineering education research focused on the incorporation of computational methods into materials science and engineering curricula over the past seven years, and have published a number of academic articles on the topic of computational learning in the context of materials science and engineering instruction.

Target Audience

Faculty or instructors

Course Description and Approach

Modeling and simulation is regarded as the "third pillar" of science, but it has not yet earned a prominent place in undergraduate engineering classrooms. Meanwhile, modern engineering workplaces now use modeling and simulation practices, coupled with computational tools, to aid the analysis and design of systems. To address these shifts, this workshop will guide faculty to integrate computational practices sooner and more often in the undergraduate engineering curriculum.

The workshop proposes a Computational Pedagogical Disciplinary Knowledge framework, which allows faculty to blend modeling, simulation and computational skills with disciplinary knowledge, along with effective pedagogical practices to teach such concepts and skills. We adapted Koehler and Mishra’s (2009) Technological Pedagogical Content Knowledge for this purpose.  Our goal is to use this framework as a guidance for the effective integration of computational tools and practices into the undergraduate engineering curriculum.  The components of our framework as shown in Figure 1 are:

Computational Knowledge—Knowledge about certain ways of thinking about modeling and simulation. This includes aspects of computational thinking, computational practices, computational tools and computational methods and techniques.

Disciplinary (Content) Knowledge—Also called Content Knowledge, refers to faculty knowledge about the subject matter to be learned or taught. The content to be covered includes concepts, theories, ideas, knowledge of evidence and proof, as well as established practices and approaches toward developing such knowledge.

Pedagogical Knowledge—Refers to knowledge about the processes and practices or methods of teaching and learning. They encompass learning theories, instructional design frameworks, scaffolding methods and learning strategies, among others.

Our focus for the workshop will be primarily on the development of pedagogical knowledge (PK) and how to integrate that with disciplinary knowledge and computational knowledge. Due to faculty’s extensive expertise in disciplinary knowledge, this topic will not be covered in the learning objectives. However, examples showing areas of application in engineering will be discussed.

Specific Learning Objectives

  1. Recognize the different audiences of computing and derive computation learning objectives accordingly.
  2. Identify disciplinary learning objectives that can be made more accessible using computational tools.
  3. Align the identified disciplinary learning objectives with modeling and simulation practices for specific target learners.
  4. Identify scaffolding strategies to help learners overcome possible challenges associated with modeling and simulation practices.
  5. Design or adopt assessment mechanisms to identify learning gains and computational proficiency.

About the Speakers

Michael Falk is a professor of materials science and engineering, mechanical engineering and physics at Johns Hopkins University where he serves as Vice Dean for Undergraduate Education. He has published over 75 peer-reviewed articles in the materials science and engineering education literature. He has taught at Johns Hopkins University and at the University of Michigan for 18 years.

Alejandra J. Magana is an associate professor of computer and information technology and engineering education at Purdue University. Her research focuses on the incorporation of computation into engineering education and the development of computational fluency by students. She has published 47 refereed journal articles along with more than 60 peer-reviewed conference proceedings in the science, engineering and computing education literature.