Science and engineering (S & E) programs frequently claim that they teach undergraduate students how to be good problem solvers. However, there has been little research to-date that demonstrates this, in no small part due to the fact that measuring problem-solving is quite difficult. Our recent work characterized experts' problem-solving as carrying out a series of several dozen decisions made in solving novel problems; these decisions are remarkably consistent across S & E disciplines. Based on this, we developed a template for an assessment that measures students' problem-solving skills by posing questions that require them to make a subset of these expert decisions in suitable contexts. Preliminary results show that the assessment captures a wide range of problem-solving skills among students, and exposes key weaknesses in problem-solving. Most importantly, the data show that students' predictive frameworks--their mental models of a system's key features and the relationships between them-are less robust than experts', limiting students' ability to make predictions and explain observations while problem-solving. We provide detailed results from a pilot-test of this assessment in the context of chemical engineering design, which applies many concepts from physics. These results point to a general deficiency in undergraduate S & E programs: students are not being given the opportunity to practice expert decision-making, and thus do not develop robust predictive frameworks by the end of their undergraduate programs.