The Engineering Problem Solving Process: Good For Students?

Abstract

As part of an ongoing effort to better understand student problem-solving processes to openended problems, we have coded 14 mechanical engineering projects (representing about 60 journals) according to abstraction level, design activity, planning, and reporting. We also developed quantitative outcome measures that are reported in a separate submission to this conference. We then developed a computer model of the journal data that correlates 12 key process variables to design outcomes, and conducted a computer design of experiments to extract the effects that the process variables have on the response variables (i.e., project outcomes). In this paper we report the results of this modeling effort and discuss their implications for the general model of engineering problem-solving presented in various forms in many engineering textbooks. Our results suggest modifications to the engineering problem-solving model to make it more suitable for engineering students. Solving open-ended problems is arguably the cornerstone of the engineering endeavor. Employers look for engineers who are effective at solving open-ended problems. Engineering accreditation demands evidence that students can tackle open-ended problems proficiently. Much faculty effort is devoted to improving student skills in this area. The basic process model used for these kinds of problems starts with identification of need, then goes through information gathering, idea generation, evaluation and selection steps—in other words, a basic design process model. Our three-year study of student design processes suggests that the general model for engineering problem-solving may require some tweaking to make it a more effective model for engineering students. Over the decades, numerous models have been proposed to describe “the engineering design process.” However, few of these have been empirically validated or experimentally verified. Most have been developed through personal experience and accumulation of anecdotes. Furthermore, few models explicitly consider student processes relative to project outcomes. Our work attempts to further our understanding of problem-solving processes by gathering data from actual projects (one in which the participants have real stakes) in large enough sample sizes to enable statistical modeling that directly links design process to outcome. In this study, we analyzed data collected from 14 student mechanical engineering design projects, relating design process variables to project outcomes using statistical techniques. Our aim was to better understand what process characteristics tend to be associated with good design outcomes.