Thinking rock subgoal setup7/4/2023 ![]() ![]() This discussion of problem solving is structured around important findings from DBER that are consistent with prominent themes from the cognitive science literature, namely problem representation and the nature of the solution process. Students who have scant experience with ill-defined problems during their undergraduate education may be poorly prepared to grapple with the most significant problems in their fields. Consider two examples: (1) How can the rapid regrowth of human skin be promoted so that life-threatening infections in burn patients are prevented? (2) How can affordable, alternative energy to power cars be generated, thereby limiting reliance on fossil fuels? These are the kinds of problems students will have to solve after they graduate. Society’s most important problems are usually ill-defined in some way. For example, what constitutes a better coffee cup, and how does one decide that a new cup design represents a big enough improvement over the status quo to declare the design finished? For an engineering problem, the goal may be ill-defined as a result, it may not be clear how to determine whether the goal has been accomplished. In a laboratory, the means of generating the solution may be ill-defined. For other types of problems, however, such as a more open-ended laboratory or an authentic design problem in engineering, students have to define one or more of the problem components on their own (Fay et al., 2007 Whitson, Bretz, and Towns, 2008). In these problems, the initial conditions, the goal, the means for generating and evaluating the solution, and the constraints on the solution are all clearly specified for students. Most of the problems students encounter in their science and engineering classes are well-defined, such as a mechanics word problem. Researchers in numerous disciplines have drawn a distinction between well-defined and ill-defined problems (Hsu et al., 2004 Reitman, 1965). Clearly, problem solving is central to science and engineering as well to everyday life. ![]() This characterization describes much of what people do on a daily basis, from (a) mundane activities like deciding what to cook for dinner given the ingredients at hand or how to get from work to home given certain street closures, to (b) student activities such as interpreting laboratory results, figuring out how to organize a term paper on evidence for speciation, or designing a roller coaster for an engineering class, to (c) professional work such as curing illnesses or determining the best way to structure a class so that students will understand a key concept. That is, during problem solving the path to the intended goal is uncertain. ![]() Previously learned steps from memory (Bassok and Novick, 2012 Martinez, 2010). It is required whenever there is a goal to reach and attainment of that goal is not possible either by direct action or by retrieving a sequence of Problem solving may be the quintessential expression of human thinking. The discussion of each topic concludes with an identification of directions for future research. We then discuss the research from each discipline and summarize key findings across disciplines. Following these introductions, we provide an overview that summarizes the focus of DBER on the topic, the theoretical frames in which DBER is grounded, and the typical methods used. The discussion of each topic in this chapter begins with an introduction of that topic and its importance to undergraduate science and engineering education. ![]() Although we recognize that there are other important dimensions to promoting a deep understanding of science and engineering-including strong mathematical knowledge-we start with these topics because they are vital to acquiring greater expertise in the disciplines, and discipline-based education research (DBER) on them is relatively extensive and robust. This chapter addresses how students use those understandings to solve problems, and how scientific representations, such as pictures, diagrams, graphs, maps, models, and simulations facilitate or impede students’ problem solving and understanding of science and engineering. Chapter 4 explored students’ conceptual understanding in science and engineering, with the goal of helping students advance toward a more expert-like understanding. ![]()
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