Pros and Cons of Using Simulation Games to Teach Climate ChangeIntroduction: Topic: Climate Change and Global Warming This topic involves many complex phenomena and uncertainties. The issues involving climate change not only arise from uncertainties in underlying science, but also from uncertainties about behavioral, economic and political factors. Also, global warming is usually too slow for individuals to recognize, thus making it a hard concept to create concrete understanding. Therefore, simulators are a great way to address the challenge. Something to keep in mind is that a simulation itself cannot replace other learning materials as it cannot depict every aspect of the issue: “a simulation has to choose a very small subset of characteristics around which to build its representation” (Salen & Zimmerman, 2003 ch. 27). Therefore, the simulation is a tool to complement the study of a complex subject matter, but cannot be used alone as the main and only study tool. In this example, we will look at “Climate Challenge” and its features. In the Climate Challenge simulation game, the player takes the role of the President of the European Nations. The President decides on policies and handles international negotiations that change the fate of the European economy, resources and environment. In order to win this game, the player must reduce CO2 emissions. How does this simulation contribute to the learning objectives? The simulator uses mathematical models based on carbon dioxide emission forecasts produced by the Intergovernmental Panel on Climate Change (IPCC). One of the challenges of this subject is that global changes usually happen too slowly for individuals to recognize, but accumulated human knowledge, together with further scientific research, can help people learn more about these challenges and guide their response (Keller & Quinn, 2012). The climate models are an important tool to forecast change over a short period of time and help us make decisions based on the impact of human actions on the earth. This simulation helps students to understand the complex model of stability and change, and at the same time provides potential solutions to climate change. Exploring explanatory feedback in the simulation Most class materials provide a static representation of the concept, however the interactive simulation provides gradual change based on our actions. Through operation and configuration, the students get real-time feedback on the complex global change system. The simulation provides explanatory feedback that deepens the students’ understanding of the subject (Johnson & Priest, 2014). At the end of each round, the President gets a newspaper report on how people reacted to the policies chosen and the President’s voter approval rate based on their policies. This helps the students to reflect on their choices and their choices’ effectiveness in a different form of evaluation. The game provides immediate feedback on the degree of political approval (bottom right corner) when a certain action card is selected. The game also reveals immediately how the resources would change by selecting each card. According to an experiment by Moreno and Valdez (2005), the immediate feedback in an interactive environment may have a negative interactivity effect. When players are shown immediate feedback, they may abandon learning objectives and proceed with simple “trial and error” strategy, such as selecting the card that reflects the best approval rate, or selecting a card that reflects best the emission reduction. Doing this as a class activity helps to prevent this behavior, as the teacher acts as a guide to help students discuss why such choices have been made and why such choices are a good solution to the current problem. Fiorella, Vogel-Walcutt, and Schatz (2012) examined the modality principle when feedback was provided to students in a complex simulation-based training task and proved that spoken feedback was better than print feedback for learning (Mayer, 2014). The simulation provides only print feedback with no sound effects either. Therefore, the simulation could be improved by the teacher reading the printed feedback outloud. Students with low spatial ability have trouble understanding long explanatory feedback (Johnson & Priest, 2014); the tutorial section of the simulation provides a guided instruction to how to use the simulation, which reduces some of the processing load of a long explanation. It could be improved with a in-game worked-example of the first round to help students with low spatial ability overcome the cognitive load on working memories. A worked-example guided by a live instructor would also help to overcome this. The simulation game creates a guided discovery-based learning environment (Moreno, 2004). The teacher and the simulator prompts are designed to elicit self-explanations (“which variables are you most sure and least sure will have an effect on the emission goal?”) and reason justification (“for the variable you are most sure will have an effect, why do you think so?”). The prompts will help the students through their inquiry process. Process constraints can be a useful tool in this phase of learning to reduce the complexity of discovery-based learning (Jong & Lazonder, 2014). Simulators are a great way to include model progression that allow students to explore one variable at a time while gradually increasing the complexity of the task. References:
Johnson, C. I., & Priest, H.A. (2014). The Feedback Principle in Multimedia Learning. In R. Mayer (Ed.), The Cambridge Handbook of Multimedia Learning (2nd ed., 449-463). New York: Cambridge University Press. Jong, T., &Lazonder, A., (2014) The Guided Discovery Learning Principle in Multimedia Learning. In R. Mayer (Ed.), The Cambridge Handbook of Multimedia Learning (2nd ed., pp. 371-390). Cambridge: Cambridge University Press. Keller, Quinn, (2012) A framework for K-12 science education : practices, crosscutting concepts, and core ideas. Logan Fiorella, Jennifer J. Vogel-Walcutt, & Sae Schatz. (2012). Applying the modality principle to real-time feedback and the acquisition of higher-order cognitive skills. Educational Technology Research and Development, (2), 223. Mayer, R. E. (2014). Cognitive theory of multimedia learning. In R. Mayer (Ed.), The Cambridge Handbook of Multimedia Learning (2nd ed., pp. 63). Cambridge: Cambridge University Press. Zimmerman, E., & Salen, K. (2003). Rules of play: Game design fundamentals. Boston, MA: MIT Press.
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