Why Teach with Simulations?

Deep Learning

Instructional simulations have the potential to engage students in "deep learning" that empowers understanding as opposed to "surface learning" that requires only memorization. A good summary of how deep learning contrasts with surface learning is given at the Engineering Subject Centre: Deep and Surface Approaches to Learning. Deep learning means that students:

Learn scientific methods including

  • the importance of model building. Experiments and simulations are the way scientists do their work. Using instructional simulations gives students concrete formats of what it means to think like a scientist and do scientific work.
  • the relationships among variables in a model or models. Simulation allows students to change parameter values and see what happens. Students develop a feel for what variables are important and the significance of magnitude changes in parameters.
  • data issues, probability and sampling theory. Simulations help students understand probability and sampling theory. Instructional simulations have proven their worth many times over in the statistics based fields. The ability to match simulation results with an analytically derived conclusion is especially valuable in beginning classes, where students often struggle with sampling theory. Given the utility of data simulation, it is not surprising that SERC has an existing module on teaching with data simulation.
  • how to use a model to predict outcomes. Simulations help students understand that scientific knowledge rests on the foundation of testable hypotheses.

Learn to reflect on and extend knowledge by

  • actively engaging in student-student or instructor-student conversations needed to conduct a simulation. Instructional simulations by their very nature cannot be passive learning. Students are active participants in selecting parameter values, anticipating outcomes, and formulating new questions to ask.
  • transferring knowledge to new problems and situations. A well done simulation is constructed to include an extension to a new problem or new set of parameters that requires students to extend what they have learned in an earlier context.
  • understanding and refining their own thought processes. A well done simulation includes a strong reflection summary that requires students to think about how and why they behaved as they did during the simulation.
  • seeing social processes and social interactions in action. This is one of the most significant outcomes of simulation in social science disciplines such as sociology and political science.

Simulation Works

Simulations are among the most often used pedagogies in industry and government.
  • Airlines require pilots to log simulator hours, electrical engineers conduct simulations on a daily basis to check load requirements, the Pentagon simulates potential conflicts, and so on.
  • Given the success in industry and government, it is not surprising that simulation is found in professional schools in universities. Medical students, for example, learn on plastic patients that are programed to exhibit all manner of symptoms in rapid succession. A few select business schools have a resource like the Hughey Center for Financial Services at Bentley University, where a former alum donated money to recreate a Wall Street trading room, complete with the ability to simulate any market event. Health educators are using entertainment style games and simulations and social networking tools to construct effective learning environments in the classroom and online, Kaufman and Lauve (2010).

While cost constraints limit the large scale simulations found in the corporate environment, Hertel and Millis (2002) argue that much of what is successful outside of the academy can be extended to undergraduate instruction with careful curriculum development. Their extensive work with simulation in the Department of Defense suggests a series of steps to ensure simulations bridge the gap between theory and reality in ways that are meaningful to students. While much of their evidence for the success of simulations is generated by matching simulation curriculum with deep and active leaning features, their suggestion that successful simulations force instructors to have refined curricular goals that are made clear to students in writing is clearly a necessary condition for the student learning goals given in the preceding section.

More directly focused on learning theory, Bransford, Brown, and Cocking (2000) develop general pedagogical prescriptions that work well with instructional simulations. Changing routine experts into adaptive experts requires that students learn how to transfer knowledge to new problems and situations. Simulations also can make students aware of their own thought processes and how they arrive at conclusions.

Finally, Porter et. al. (2004) summarize what is known about the learning effectiveness of simulations in economics principles courses. Their general conclusion is that simulation either makes no difference or a small amount of positive difference. There are suggestions in the various economics studies, however, that instructional simulations may be more effective for some students than the general results suggest.

  • There is some evidence that students who think in a scientific manner apply this thinking to a simulation and benefit, while other students do not. Shute, Glaser, and Raghavan (1990), Katz and Ochs (1993).
  • There is some evidence that students in a class that used simulations learned a set of concepts in less time that students in a traditional, lecture based class. Shute & Glaser (1989).
Given these findings, an instructor thinking about how to improve the critical thinking of his or her students should find instructional simulations a valuable tool. The findings also suggest that upper-division courses that structure the curriculum in terms of scientific inquiry are tailor made for instructional simulations.