The explanatory modeling tasks and recommended strategies contained in the current service packages offered by GLN Consulting are refinements of student engagement activities originally used by Dr. Newsome in his own teaching. Since his retirement from teaching, these activities and strategies have been refined through an extensive, integrative analysis of selected research on creative problem solving by expert scientists and college students and research on how scientific explanations enhance understanding of a target domain. The studies on creative problem solving that were selected for this analysis focus on the kinds of problem solving activities that have been shown to contribute to conceptual innovation in science and to promote greater depth of understanding by students (Clement, 2009). These kinds of problem solving activities share two important characteristics. First, they require the use of modeling procedures that go beyond the “empirical discovery” of patterns in bodies of data (observations) to include the use of theoretical constructs to derive hypotheses that explain the occurrence of those patterns. Second, they require reasoning about novel situations in ways that are not amenable to the application of practiced procedures for solving particular kinds of problems (i.e. they are nonalgorithmic). The selected research on the nature of scientific explanations includes philosophical analyses of the construction and use of explanations by scientists across disciplines.
The selected studies of expert problem solving by scientists focus on (1) examining the problem solving activities that advance scientific understanding and (2) aligning these activities with those that nonscientists use to solve problems and understand the world. These studies use methods and analytic techniques from history, philosophy, and cognitive science to establish a cognitive basis for creative scientific reasoning that is continuous with everyday reasoning activities. Historical records of the practices of preeminent scientists and ethnographic observations of current scientific practices provide information about the kinds of problem solving activities that promote theoretical understanding within the scientific community. Philosophical analysis of these problem solving practices describe them as forms of nonformal reasoning like analogical modeling and simulative model-based reasoning. Cognitive analyses of these “model-based reasoning” practices help to align them with the cognitive resources’ and limitations that scientists share with other humans.
Taken together, the results of these interdisciplinary studies indicate that advances in theoretical understanding can be produced through a dynamic, incremental process of model construction, manipulation, evaluation, and revision. Traditional notions of scientific reasoning as the application of symbolic and mathematical logic are too narrowly constrained to account for advances in theoretical understanding. To account for conceptual innovation and change, these philosophical notions of reasoning must be extended to include analogical reasoning activities that involve model construction, evaluation, and adaptation. Models are representations of phenomenon, systems, or situations that highlight epistemicaly relevant features of these targets. The function of a model is to afford epistemic access to problem relevant features of the target and display the significance of those features to potential problem solutions. Theory development and change in science typically involves the construction, manipulation, evaluation, and revision of dynamic models that serve as structural or functional analogs of real world systems rather than axiomatic systems or propositional networks (Darden, 1991; Giere, 1988; Morgan & Morrison, 1999). The scientific practices that were found to contribute to advances in theoretical understanding share several key features that are invariant across scientific disciplines. Cognitive analyses of these key features suggest that they reflect the extension and refinement of the human capacity for simulative thinking through modeling (e.g. Dunbar, 2001, 2002; Nersessian, 2005, 2008).
Several studies have shown that simulative model-based reasoning plays a key role in the thinking of both scientists and students (e.g. Clement, 2009; Dunbar, 2001; Nersessian, 1995). The studies selected for analysis examined the extent to which nonformal reasoning processes used by scientists to advance theoretical understanding occur naturally in students and how students’ ability to use these processes can be utilized in instruction. The primary focus of these selected studies was on students’ use of the kinds of representational and inferences processes employed in scientific practice as opposed to the more formal expressions that appear in scientific articles. The results of these studies indicate that students have the natural ability to perform most of the nonformal reasoning activities that contribute to conceptual change in science and learning to perform these activities can help them to achieve greater depth of understanding of a scientific discipline. These results also indicate that the kinds of model-based reasoning activities that contribute most to the achievement of theoretical understanding in students are those that involve the generation, manipulation, evaluation, and adaptation of models that represent explanatory mechanisms.
Philosophers of science have long debated the nature of explanation, but most contemporary ones agree that explanations enhance understanding of phenomena by situating them within the organizational structure (logical or causal) of a target domain (Salmon, 1998; Strevens, 2008). The studies selected for analysis are those that focus on the construction and use of explanations by psychologists (e.g. Bechtel, 2008; Cummins, 1883) and neuroscientists (e.g. Craver, 2001, 2007; Machamer, Darden, & Craver, 2000).
Bechtel, W. (2008). Mental mechanisms. Lawrence Erlbaum Associates.
Clement, J. (2009). Creative model construction in scientists and students. Springer-Verlag.
Craver, C. (2001). Role functions, mechanisms, and hierarchy. Philosophy of Science, 68, 53-74.
Craver, C. (2007). Explaining the brain: Mechanisms and the mosaic unity of neuroscience. Oxford
Cummins, R. (1983). The nature of psychological explanation. MIT Press.
Darden, L. (1991). Theory change in science: Strategies from Mendelian genetics. Oxford University
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Giere, R. (1988). Explaining science: A cognitive approach. University of Chicago Press.
Machamer, P. Darden, L., & Craver, C. (2000). Thinking about mechanisms. Philosophy of Science, 67,
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Nersessian, N. (1995). Should physicists preach what they practice?: Constructive modeling in doing and learning physics. Science and Education, 4, 203-226.
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Strevens, M. (2008). Depth: An account of scientific explanation. Harvard University Press.