When students use a model of behavior to gain a better understanding of that behavior, they are doing a simulation. For example:
- When students are assigned roles as buyers and sellers of some good and asked to strike deals to exchange the good, they are learning about market behavior by simulating a market.
- When students take on the roles of party delegates to a political convention and run the model convention, they are learning about the election process by simulating a political convention.
- When students create an electric circuit with an online program, they are learning about physics theory by simulating an actual physical set-up.
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This teaching strategy has the potential to engage students in “deep learning” that empowers understanding as opposed to “surface learning” that only requires memorization. Deep learning means that students learn scientific methods including:
- The importance of model building.
- The relationships among variables in a model or models.
- Data issues, probability and sampling theory.
- How to use a model to predict outcomes
- Instructor Preparation: Instructional simulations can be very effective in promoting student understanding but often require intensive lesson preparation.
- Active Student Participation: Students must actively engage in problem-solving in order for the instructional simulation to be an effective learning tool.
- Post-Simulation Discussion: Once the simulation is complete, students need sufficient time to reflect on the simulation results.
- Students can use simulations to make predictions about the social, economic or natural world.
- Simulations actively engage students with each other and with the instructor through discussion.
- Simulations allow students to transfer their knowledge to new problems and situations.
- This strategy also helps students understand and refine their own thought processes.
- Simulation exercises allow students to see social processes and social interactions in action.