Working Together: Integrating Computational Modeling Approaches to Investigate Complex Phenomena
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Working Together: Integrating Computational Modeling Approaches to Investigate Complex Phenomena Tom Bielik1 · Ehud Fonio2 · Ofer Feinerman2 · Ravit Golan Duncan3 · Sharona T. Levy4 Accepted: 23 September 2020 © The Author(s) 2020
Abstract Complex systems are made up of many entities, whose interactions emerge into distinct collective patterns. Computational modeling platforms can provide a powerful means to investigate emergent phenomena in complex systems. Some research has been carried out in recent years about promoting students’ modeling practices, specifically using technologically advanced tools and approaches that allow students to create, manipulate, and test computational models. However, not much research had been carried out on the integration of several modeling approaches when investigating complex phenomena. In this paper, we describe the design principles used to develop a middle school unit about ants’ collective behavior that integrates three modeling approaches: conceptual drawn models, agent-based models, and system dynamics models. We provide results from an initial implementation of an 8th grade curricular unit, indicating that students engaged with several aspects of the modeling practice. Students’ conceptual knowledge about ant pheromone communication increased following learning the unit. We also found gains in students’ metamodeling knowledge about models as tools for investigating phenomena. We discuss the affordances and challenges of engaging students with several modeling approaches in science classroom.
Introduction Science is about explaining the natural world. These explanations often come in the form of models and theories that are grounded in evidence (Harrison and Treagust, 2000; Lehrer and Schauble 2006). In this sense, science education is not just about learning concepts. It is just as much about learning to construct models, explain, argue, and reason using evidence and models (Penner, 2000). Developing and using models are key scientific and engineering practices (National Research Council [NRC], 2012). Models serve to explain and predict phenomena, and scientists use evidence to support or refute alternative models. Given that modeling plays such a central goal of science, it received much attention in science education. Students are expected to construct, use, evaluate, and revise models to make sense of phenomena or to solve problems. However, most students are not * Tom Bielik tom.bielik@fu‑berlin.de; [email protected] 1
Freie Universität, Berlin, Germany
2
Weizmann Institute of Science, Rehovot, Israel
3
Rutgers University, New Brunswick, NJ, USA
4
University of Haifa, Haifa, Israel
provided with meaningful opportunities to engage in this practice (Schwarz et al. 2009). Students’ modeling competence is typically viewed in two dimensions: modeling metaknowledge and the modeling practices (create, use, compare, validate, revise) (Chiu and Lin, 2019; Nicolaou and Constantinou, 2014; Nielsen and Nielsen, 2019). In recent years, metamodeling knowledge
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