  A primary goal of science and engineering (S & E) education is to produce
good problem solvers, but how to best teach and measure the quality of
problem-solving remains unclear. The process is complex, multifaceted, and not
fully characterized. Here we present a theoretical framework of the S & E
problem-solving process as a set of specific interlinked decisions. This theory
is empirically grounded and describes the entire process. To develop this
theory, we interviewed 52 successful scientists and engineers (experts)
spanning different disciplines, including biology and medicine. They described
how they solved a typical but important problem in their work, and we analyzed
the interviews in terms of decisions made. Surprisingly, we found that across
all experts and fields, the solution process was framed around making a set of
just twenty-nine specific decisions. We also found that the process of making
those discipline-general decisions (selecting between alternative actions)
relied heavily on domain-specific predictive models that embodied the relevant
disciplinary knowledge. This set of decisions provides a guide for the detailed
measurement and teaching of S & E problem-solving. This decision framework also
provides a more specific, complete, and empirically based theory describing the
practices of science.
