Model-Based Knowing: How Do Students Ground Their Understanding About Climate Systems in Agent-Based Computer Models?
This paper gives a grounded cognition account of model-based learning of complex scientific knowledge related to socio-scientific issues, such as climate change. It draws on the results from a study of high school students learning about the carbon cycle through computational agent-based models and investigates two questions: First, how do students ground their understanding about the phenomenon when they learn and solve problems with computer models? Second, what are common sources of mistakes in students’ reasoning with computer models? Results show that students ground their understanding in computer models in five ways: direct observation, straight abstraction, generalisation, conceptualisation, and extension. Students also incorporate into their reasoning their knowledge and experiences that extend beyond phenomena represented in the models, such as attitudes about unsustainable carbon emission rates, human agency, external events, and the nature of computational models. The most common difficulties of the students relate to seeing the modelled scientific phenomenon and connecting results from the observations with other experiences and understandings about the phenomenon in the outside world. An important contribution of this study is the constructed coding scheme for establishing different ways of grounding, which helps to understand some challenges that students encounter when they learn about complex phenomena with agent-based computer models.
KeywordsModel-based learning Socio-scientific issues Climate change Complex systems Grounded cognition Agent-based models Science education
The research discussed in this paper has been funded by grants to the first and third authors from the Australian Research Council Linkage program, LP100100594, and from the Curriculum Learning and Innovation Centre in the New South Wales Department of Education and Communities. We acknowledge the support from the teachers in the collaborating school. Also, we thank Dr. Kate Thompson, Dr. Polly Lai and Dr. Paul Sokes for their assistance with various aspects of this project. Finally, we greatly appreciate the feedback from our international collaborators on this project, Professor Uri Wilensky and Dr. Sharona Levy, on the NetLogo agent-based models we developed and on the overall program of research we are conducting.
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