June 20, 2010
June 20, 2010
June 23, 2010
15.232.1 - 15.232.15
Bioengineering Design Process: Patterns That Lead To Quality Outcomes Abstract To improve design education, engineering instructors need to better understand how student teams navigate that process from concept to prototype. In this research, we examined the bioengineering design process and empirically modeled how engineering teams progressed from initial conception to prototype to determine the extent to which resultant design artifacts are a function of the process used. To do this we collected data from 26 two-term senior capstone engineering projects. The data consisted of twice weekly reflections of the activities that student teams engaged in during their design process, as well as open-ended comments about their design progression. This data was then collapsed into Dym’s model from which empirical associations were made between the various stages. Coupled with the teams’ open-ended weekly reflections, we were able to identify educational patterns that potentially lead to higher or lower quality designs. Based on their final artifact, teams were judged to be innovative or non- innovative. We found that differences exist between those teams innovative non-innovative teams. This paper reports these findings.
Introduction Innovation is highly important as competition between companies and countries continues to increase. The design process is at the heart of innovation; and according to Smith and Tjandra1, in order to improve design, one needs to understand it; and one possible way to achieve this is modeling the process. Hence, it is our assertion that increasing innovation begins with improving the design process and specifically understanding and improving engineering design education.
The overall objective is to improve the effectiveness and innovativeness of design processes. In doing so, we have developed a conceptual model based on Dym’s model2 to investigate the relationships between the design activities conducted over time and their corresponding outcomes. Associations among the various design activities were then empirically analyzed; and from this we identified patterns of “good” and “poor” innovation in designs.
Specifically, the following research questions have been investigated while developing this conceptual model.
1. Do relationships exist between the various sequences of design activities and do these sequences relate to the innovation of the design outcome? 2. Given that some design activities are crucial to the process, is there a relationship between when these “most important activities” occur and the innovation of the design outcome? 3. Do exogenous factors affect the overall innovation of the design (i.e., mentor, advisor, prior internships, work experience, team contribution, etc.)?
We addressed each of these questions utilizing data obtained from 26 bioengineering students at two institutions as they progressed from concept to working prototype for their senior capstone design projects. Through this research, we have identified potential patterns and factors that lead to innovation (as evaluated by their instructors) in senior bioengineering designs.
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