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A primary goal of science and engineering (S & E) education is to producegood problem solvers, but how to best teach and measure the quality ofproblem-solving remains unclear. The process is complex, multifaceted, and notfully characterized. Here we present a theoretical framework of the S & Eproblem-solving process as a set of specific interlinked decisions. This theoryis empirically grounded and describes the entire process. To develop thistheory, we interviewed 52 successful scientists and engineers (experts)spanning different disciplines, including biology and medicine. They describedhow they solved a typical but important problem in their work, and we analyzedthe interviews in terms of decisions made. Surprisingly, we found that acrossall experts and fields, the solution process was framed around making a set ofjust twenty-nine specific decisions. We also found that the process of makingthose discipline-general decisions (selecting between alternative actions)relied heavily on domain-specific predictive models that embodied the relevantdisciplinary knowledge. This set of decisions provides a guide for the detailedmeasurement and teaching of S & E problem-solving. This decision framework alsoprovides a more specific, complete, and empirically based theory describing thepractices of science.

A Detailed Characterization of the Expert Problem-Solving Process in Science and Engineering; Guidance for Teaching and Assessment