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Science as Modeling Strategies

Modeling and argumentation strategies emerged somewhat concurrently in association with 1990s science education reform efforts (American Association for the Advancement of Science, 1993). In the mid-2000s, the NRC (2007) and others (e.g., Windschitl, Thompson, & Braaten, 2008) presented scientific modeling and theory building as a classroom practice that more authentically represented the scientific enterprise than the oft-used, and so-called, scientific method. The NRC (2007) specifically recommended that teachers should present science “as a process of building theories and models using evidence, checking them for internal consistency and coherence, and testing them empirically” (p. 5). Therefore, for many lines of scientific inquiry, modeling is a quintessential process (Nersessian, 2008). The NRC (2012) more recently stated that scientists use models as tools to “visualize and understand a phenomenon under investigation” (p. 56). Furthermore, models that deepen understanding have compatibility among a variety of scientific fields, are parsimonious, are supported by scientific evidence, and have predictive power (i.e., are supported by future lines of empirical evidence; Pluta, Chinn, & Duncan, 2011). In general, scientific models help people understand “the way the natural and human-engineered world operates” and act as “simplifications of complex laws or theories that [people translate] as general ideas” (Moulding, Bybee, & Paulson, 2015, pp. 62-63).

The science as modeling strategy extends the use of models (including physical, pictorial, graphical, mathematical, and computerized replications of phenomena) to the central focus in inquiry-based learning. Schauble (2018) views science as modeling as a bottom-up strategy for science learning, where students develop models in specific domains, even specific topics, through which they then develop a more general set of heuristics for scientific knowledge construction. Therefore, science instruction must include classroom practice in model use, together with the metacognitive aspects of modeling in science. To encompass the two components of practical use and metacognitive reflection of scientific models, Schwarz et al. (2009) proposed that students simulate scientific activities by constructing models via extant evidence and theory, using models to explain and predict, evaluating models through their ability to predict future observations, and revising models to increase their explanatory and predictive power. In doing so, the science as modeling strategy facilitates learners’ participation in scientific discourse and reasoning, critical thinking about alternative representations, and reflectivity about their own understanding (Louca & Zacharia, 2012). Promoting deep cognitive and metacognitive engagement with science content has the potential to move learners’ model use beyond purely illustrative and demonstrative purposes toward more meaningful scientific knowledge construction.

Researchers and practitioners often link the modeling as science strategy with argumentation. Windschitl et al. (2018) suggested that the evidence-based argumentation strategy informs students’ participation in scientific modeling. These researchers see the practice of argumentation in simultaneously developing models that are predictive of a phenomenon and written explanations that provide additional insight into the phenomenon. For example, consider a situation in which students use a simple computer simulation to predict future climate change and develop a written explanation of the causes of current climate change and future impacts. While doing this work, students could also engage in collaborative argumentation that critiques and parameterizes model uncertainties to develop a revised explanation that establishes a range of possible impacts. Mendonca and Justi (2013) found that students can engage in scientific argumentation when they justify their initial model construction, further calibrate the model with extant evidence, test the model with novel evidence, and evaluate the strengths and weaknesses of the model in explaining a phenomenon. Furthermore, their classroom-based research is consistent with others’ linking modeling and argumentation (for an overview, see Jimenez-Aleixandre & Erduran, 2008). However, a more recent review of the linkage of modeling and argumentation found relatively few connections between the two strategies in the literature (Campbell, Oh, Maughn, Kiriazis, & Zuwallack, 2015), with classroom implementation also of likely low frequency. One reason for the lack of connection may be that these learning and teaching strategies are challenging to implement. Much like other strategies that require higher-order thinking skills, argumentation, modeling, and scientific inquiry may be quite difficult for students to learn and for teachers to teach (see, for example, Erduran & Dagher, 2014; Klopfer, 1969). Because of this difficulty, instructional scaffolds may be required to help students learn how to fully engage in science learning strategies. Such scaffolds include, but are not limited to, the use of computer-based tools to promote scientific thinking (Greene, Hutchison, Costa, & Crompton, 2012); employment of teacher moves to promote scientific discourse and argumentation (Duschl, 2008; Li et al., 2016); and regular assignment of science journals in which students can build an ongoing record of their investigations (Sandoval & Reiser, 2004).

 
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