scholarly journals The Impact of a Hybrid Instructional Design in a First-year Design (Cornerstone) Course on Student Understanding of the Engineering Design Process

2020 ◽  
Author(s):  
Susan Donohue
Author(s):  
Ali Kamyab ◽  
Kemper E. Lewis

Modern design methodologies have used Function Component Matrices in a variety of different ways in order to support various facets of an engineering design process. The mapping of functions to components can be used to model and capture the dependencies and relationships that exist. This process is accomplished by breaking down complicated functions into smaller, easier to understand functions. This decomposition allows engineers to get a better understanding for how a change in each component within a product will affect the overall operation of the product. Being able to recognize the impact of the propagation of a sub-function change will give designers a better understanding of the flexibility (or lack thereof) of choices they have when designing a product for customization. In turn they can be used to inform the consumer regarding the consequences their customization choices can have on the final product. This paper discusses how a Functional Component Matrix (FCM) can be used to assist in this process of product customization and understanding change propagation.


Author(s):  
Mohammad Alsager Alzayed ◽  
Scarlett R. Miller ◽  
Jessica Menold ◽  
Jacquelyn Huff ◽  
Christopher McComb

Abstract Research on empathy has been surging in popularity in the engineering design community since empathy is known to help designers develop a deeper understanding of the users’ needs. Because of this, the design community has been invested in devising and assessing empathic design activities. However, research on empathy has been primarily limited to individuals, meaning we do not know how it impacts team performance, particularly in the concept generation and selection stages of the design process. Specifically, it is unknown how the empathic composition of teams, average (elevation) and standard deviation (diversity) of team members’ empathy, would impact design outcomes in the concept generation and selection stages of the design process. Therefore, the goal of the current study was to investigate the impact of team trait empathy on concept generation and selection in an engineering design student project. This was accomplished through a computational simulation of 13,482 teams of noninteracting brainstorming individuals generated by a statistical bootstrapping technique drawing upon a design repository of 806 ideas generated by first-year engineering students. The main findings from the study indicate that the elevation in team empathy positively impacted simulated teams’ unique idea generation and selection while the diversity in team empathy positively impacted teams’ generation of useful ideas. The results from this study can be used to guide team formation in engineering design.


Author(s):  
Katie Heininger ◽  
Hong-En Chen ◽  
Kathryn Jablokow ◽  
Scarlett R. Miller

The flow of creative ideas throughout the engineering design process is essential for innovation. However, few studies have examined how individual traits affect problem-solving behaviors in an engineering design setting. Understanding these behaviors will enable us to guide individuals during the idea generation and concept screening phases of the engineering design process and help support the flow of creative ideas through this process. As a first step towards understanding these behaviors, we conducted an exploratory study with 19 undergraduate engineering students to examine the impact of individual traits, using the Preferences for Creativity Scale (PCS) and Kirton’s Adaption-Innovation inventory (KAI), on the creativity of the ideas generated and selected for an engineering design task. The ideas were rated for their creativity, quality, and originality using Amabile’s consensual assessment technique. Our results show that the PCS was able to predict students’ propensity for creative concept screening, accounting for 74% of the variation in the model. Specifically, team centrality and influence and risk tolerance significantly contributed to the model. However, PCS was unable to predict idea generation abilities. On the other hand, cognitive style, as measured by KAI, predicted the generation of creative and original ideas, as well as one’s propensity for quality concept screening, although the effect sizes were small. Our results provide insights into individual factors impacting undergraduate engineering students’ idea generation and selection.


Sign in / Sign up

Export Citation Format

Share Document