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Evaluating the Impacts of Different Interventions on Quality in Concept Generation

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Conference

2016 ASEE Annual Conference & Exposition

Location

New Orleans, Louisiana

Publication Date

June 26, 2016

Start Date

June 26, 2016

End Date

August 28, 2016

ISBN

978-0-692-68565-5

ISSN

2153-5965

Conference Session

Idea Generation and Creativity in Design

Tagged Division

Design in Engineering Education

Page Count

16

DOI

10.18260/p.26766

Permanent URL

https://peer.asee.org/26766

Download Count

224

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Paper Authors

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Kevin Charles Helm The Pennsylvania State University

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Kevin Helm is a graduate student at The Pennsylvania State University. Since Fall 2014, he has studied cognitive research in engineering design with support from Dr. Kathryn Jablokow. He received a B.S. in Mechanical Engineering in 2015 from the Schreyer Honors College at Penn State.

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Kathryn W. Jablokow Pennsylvania State University

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Dr. Kathryn Jablokow is an Associate Professor of Mechanical Engineering and Engineering Design at Penn State University. A graduate of Ohio State University (Ph.D., Electrical Engineering), Dr. Jablokow’s teaching and research interests include problem solving, invention, and creativity in science and engineering, as well as robotics and computational dynamics. In addition to her membership in ASEE, she is a Senior Member of IEEE and a Fellow of ASME. Dr. Jablokow is the architect of a unique 4-course module focused on creativity and problem solving leadership and is currently developing a new methodology for cognition-based design. She is one of three instructors for Penn State’s Massive Open Online Course (MOOC) on Creativity, Innovation, and Change, and she is the founding director of the Problem Solving Research Group, whose 50+ collaborating members include faculty and students from several universities, as well as industrial representatives, military leaders, and corporate consultants.

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Shanna R. Daly University of Michigan Orcid 16x16 orcid.org/0000-0002-4698-2973

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Shanna Daly is an Assistant Professor of Mechanical Engineering at the University of Michigan. She has a B.E. in Chemical Engineering from the University of Dayton (2003) and a Ph.D. in Engineering Education from Purdue University (2008). Her research focuses on strategies for design innovations through divergent and convergent thinking as well as through deep needs and community assessments using design ethnography, and translating those strategies to design tools and education. She teaches design and entrepreneurship courses at the undergraduate and graduate levels, focusing on front-end design processes.

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Eli M. Silk Rutgers, The State University of New Jersey Orcid 16x16 orcid.org/0000-0003-1248-6629

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Eli Silk is an Assistant Professor of Professional Practice in the Graduate School of Education at Rutgers, The State University of New Jersey.

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Seda Yilmaz Iowa State University Orcid 16x16 orcid.org/0000-0001-7446-3380

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Dr. Yilmaz is an Associate Professor of Industrial Design. She teaches design studios and lecture courses on developing creativity and research skills. Her current research focuses on identifying impacts of different factors on ideation of designers and engineers, developing instructional materials for design ideation, and foundations of innovation. She often conducts workshops on design thinking to a diverse range of groups including student and professional engineers and faculty member from different universities. She received her PhD degree in Design Science in 2010 from University of Michigan. She is also a faculty in Human Computer Interaction Graduate Program and the ISU Site Director for Center for e-Design.

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Rafael Suero The Pennsylvania State University

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Rafael Suero is an undergraduate student at the Pennsylvania State University. He is pursuing a double major in Mechanical and Nuclear Engineering. He joined the Ideation Flexibility Lab in Fall of 2014. He then participated in a Research Experience for Undergraduates program conducted by the College of Engineering Research Initiative at PSU, which only helped to heighten his interest in engineering design and education research. In Fall of 2015, Rafael also joined Jessica Menold in her doctoral research involving prototyping.

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Abstract

Evaluating the Impacts of Different Interventions on Quality in Concept Generation

Producing ideas of high quality has great importance in engineering design. Although concept generation is sometimes one of the shorter phases of a project, concept generation that leads to viable and unique solutions can greatly contribute to a product’s final outcomes. Concept generation also has importance as a tool for engineering education and academic research. Because the quality of solutions can vary from individual to individual and from circumstance to circumstance, it would be useful to better understand how different interventions influence the outcomes of the ideation process in the concept generation stage of engineering design. In this work, we investigated the impacts of the problem context and three specific interventions designed to increase the ideation flexibility for the outcomes of concept generation. The three interventions were problem framing, design tools, and teaming. Our results show that both problem framing and teaming impact several aspects of quality, while design tools only impact the quantity of ideas produced.

This paper investigates interventions and their impact on concept generation; its main concern is which interventions affect the quality of an individual’s ideas and in what ways. The interventions under consideration include teaming, design tools, and problem framing, as well as problem context. Problem context refers to the focus of concept generation – i.e., the given design task. In this work, four unique problem contexts were studied. The three interventions – teaming, design tools, and problem framing – were created to aid the ideation process. Teaming encourages participants to share ideas as they work in teams, and design tools provide helpful design heuristics. Problem framing alters a given problem context with respect to expectations and constraints. In combination, these interventions are intended to promote ideation flexibility, one’s ability to switch between preferred and non-preferred methods of concept generation as preferred by the problem. Given insight into how the three interventions impact idea quality, engineers, educators, and students will be able to make informed decisions about which interventions to use under different conditions with different concept generation goals in mind.

Research Method: 159 engineering students from University X, University Y, and University Z were asked to participate in two sessions of concept generation. In each session, participants generated concepts and recorded their responses using design sketches and written descriptions. The first session focused on participants’ natural creative output – no interventions were applied. During the second session, participants received the aforementioned interventions in addition to the instructions used in the first session. The cognitive styles of the participants were also assessed using the Kirton Adaption-Innovation Inventory (KAI). The creative characteristics of each participant’s ideas were then measured using quality metrics taken from the literature; these included:

1. Relevance of ideas 2. Workability of ideas 3. Specificity of ideas 4. Novelty of ideas 5. Quantity of ideas 6. Variety of ideas

The scores of each participant’s multiple ideas were averaged to determine a participant’s overall performance with respect to each measure of creative output. Then, by comparing changes in these metrics across sample groups, several questions with regard to each intervention were investigated:

1. Problem Context: How does the complexity of a problem context affect creative output? Do seemingly complex or unfamiliar contexts warrant different responses? 2. Teaming: Which aspects of creative output are benefited by teaming? Do practical concerns like relevance or workability result in different outcomes than concerns for novelty or variety? 3. Problem Framing: Are individuals with certain cognitive styles more responsive to problem framing? Do preferred styles noticeably affect all measures of creativity? Or are measures like relevance or novelty more dependent on the type of problem framing? 4. Design Heuristics: How well do participants perform using design tools? Which measures of creative output are more affected by this intervention?

Using this research method, many of these questions were answered to statistical significance, and no intervention was found to have a completely positive or negative impact on creative output. For instance, adaptive problem framing was related to improvements in relevance and workability, yet it was also related to decreases in the number of ideas generated.

The complex outcomes of these interventions suggest that engineering students, educators, and professionals need to understand the inherent trade-offs of applying interventions to concept generation. When selecting interventions, individuals should first consider which aspects of creativity warrant improvement and then decide, based on that understanding, how those goals can be best achieved.

Helm, K. C., & Jablokow, K. W., & Daly, S. R., & Silk, E. M., & Yilmaz, S., & Suero, R. (2016, June), Evaluating the Impacts of Different Interventions on Quality in Concept Generation Paper presented at 2016 ASEE Annual Conference & Exposition, New Orleans, Louisiana. 10.18260/p.26766

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