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Predictive Modeling of Cognitive Style Using Quality Metrics

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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

Design in Engineering Education Division Poster Session

Tagged Division

Design in Engineering Education

Page Count

14

DOI

10.18260/p.25940

Permanent URL

https://peer.asee.org/25940

Download Count

126

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Paper Authors

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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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Kathryn W. Jablokow The 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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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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Wesley Teerlink

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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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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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Abstract

The Ideation Flexibility project aims to expand the flexibility of idea generation in engineers so that they have a variety of ways to approach problems. The lab looks at three interventions and how they affect ideation. The interventions are as follows: • Design heuristics: broad concepts used by expert designers in exploring the solution space. • Framing: changing how the problem is stated to change its interpretation. • Teaming: putting the engineers in teams (including dyads). The lab is working on creating materials and guides to help both engineers and those educating engineers. Since ideation is the first step in the design process, the ability to approach a problem in the most effective way will likely reduce the amount of time wasted in the process, making the entire design process more cost and time effective. The lab’s work is based on Kirton’s Adaption-Innovation theory, which states that people lie on a spectrum of cognitive preference for problem solving (including ideation) that ranges from strongly Adaptive to strongly Innovative. Those who are more Adaptive prefer to solve problems using more structure, which may manifest by thinking in a stepwise fashion. Those who are more Innovative prefer to solve problems using less structure, which may manifest by thinking tangentially. This cognitive preference – called cognitive style – can be reliably assessed using the Kirton Adaption-Innovation Inventory or KAI. The KAI total score is also divided into three sub-scores: Sufficiency of Originality (SO), Efficiency (E), and Rule/Group Conformity (R/G). The lab asked pre-engineering students (senior year of high school) to sophomore engineers in college to generate ideas within 30 minutes to solve specific problems both with and without the three interventions; the “no intervention” condition is referred to as “neutral ideation”. Students recorded their design ideas using both sketches and verbal (written) descriptions. The three interventions were applied separately so that they could then be compared against neutral ideation in order to determine how the interventions affect ideation. Various other data were collected from the students, including age, race, gender, and KAI scores (i.e., cognitive style). The ideas were coded based on Quality metrics found in the literature. Quality is defined as an overall measure of how well an idea relates to and solves a problem, as well as how effectively it is communicated and how easily it can be implemented. Quality is then subdivided into three other metrics: Relevance, Workability, and Specificity. Those metrics are then further subdivided into Effectiveness and Applicability, Implementability and Acceptability, and Clarity and Implicational Explicitness, respectively. The students were also asked to reflect on each ideation activity to obtain data on their perceptions of their ideation under each experimental condition (neutral ideation and the three interventions). The purpose of this particular study was to attempt to model cognitive style based on these Quality metrics, as well as any relevant demographic data. The aim is to have a predictive model of cognitive style based on neutral ideation, which can then be used with data collected from the intervention sessions to determine whether any of the interventions have had an effect on an individual’s ideation. In other words, do the interventions affect how adaptive or innovative an individual’s ideation has become? This will give an effective measure of Ideation Flexibility. By gaining this insight into how the different interventions affect ideation, a better approach to ideation can be created. Both linear and nonlinear relationships between cognitive style (KAI) and the Quality metrics were explored. Preliminary results show that creating linear models using other factors, such as race or age, may create viable models; however, these relationships will require further research for confirmation. In addition, averaging the metrics and looking at Quantity as well as Quality metrics may also yield better results. In the end, although the regressions did not predict cognitive style (KAI scores) well enough to be used reliably, this paper will report on several interesting findings that may shed light on how cognitive style and ideation are related.

Suero, R., & Jablokow, K. W., & Helm, K. C., & Teerlink, W., & Yilmaz, S., & Daly, S. R., & Silk, E. M. (2016, June), Predictive Modeling of Cognitive Style Using Quality Metrics Paper presented at 2016 ASEE Annual Conference & Exposition, New Orleans, Louisiana. 10.18260/p.25940

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