Diversity in Design Teams: An Investigation of Learning Styles and their Impact on Team Performance and Innovation* KIMBERLY LAU Department of Mechanical Engineering, University of California at Berkeley, Berkeley, CA 94709–1742, USA. E-mail: lauk@berkeley.edu SARA L. BECKMAN Haas School of Business, University of California at Berkeley, Berkeley, CA 94709–1742, USA. E-mail: beckman@haas.berkeley.edu ALICE M. AGOGINO Department of Mechanical Engineering, University of California at Berkeley, Berkeley, CA 94709–1742, USA. E-mail: agogino@berkeley.edu In this paper, we examine the role of diversity in design team performance, and discuss how diversity factors affect the dynamics and success of a design team. In particular, we focus on diversity in learning styles, as defined by Kolb’s Experiential Learning Theory. We also consider other demographic factors, such as discipline and gender. We present data gathered over two semesters of a multidisciplinary, project-based graduate level design course offered at the University of California at Berkeley. The data were captured through a series of surveys administered during the semester, first to collect diversity information on learning styles and standard demographics, and then to assess team performance as students reflected on their team interactions. We examine and compare the overall learning style breakdown of students in the class, along with an analysis of the teams. The results of our analyses offer insights into how students with different learning styles appear to contribute to design team performance. We provide recommendations that will help inform design educators on how to enhance overall team performance and innovation, with an understanding of learning style differences. Keywords: learning styles; design teams; team performance; Kolb’s experiential learning 1. Introduction and background With ever-changing technologies and rising market competition, it is increasingly important to design innovative products. Teamwork leads to innova- tion more frequently than individual efforts [1], and organizations that focus on new product develop- ment invest in developing their teams to achieve a high level of creativity and innovation. This begs the question of how to best form and manage teams that will successfully build quality products. For example, should teams consist of experts from the same field and with similar reinforcing experi- ences, or should the teams be composed of experts from diverse backgrounds and personality types? Many companies rely on cross-functional teams to benefit from diverse perspectives, experiences, and design-for-X expertise, including members from engineering, business, industrial design, and more [2]. A variety of diversity factors may affect new product development team performance outcomes. Individual differences—be they cultural, gender, or cognitive—cause people to approach a single situa- tion in various ways. In the academic setting, such differences may influence how a person learns, solves problems, and interacts with peers and team members. In recent years, design education researchers have begun exploring the relationship between learning styles and learning in design. From this research, a variety of learning characterizations have been identified. Newland categorizes learners as com- mon sense, dynamic, contemplative, and zealous [3]. Leary classifies a person’s behavior along two axes: dominant versus submissive and friendly versus critical [4]. Felder examines learning under sensory versus intuitive, visual versus auditory, inductive versus deductive, and active versus reflec- tive dimensions [5]. In his Experiential Learning Theory (ELT), Kolb posits that a person acquires knowledge by grasping and transforming experience [6, 7]. He defines these experiences along two dialectically related continua: Concrete Experience (CE) or Abstract Conceptua- lization (AC), which measure how an individual perceives information, and Reflective Observation (RO) or Active Experimentation (AE), which mea- sure how an individual processes information. These two continua intersect to create four quad- rants, each representing a different learning style (Fig. 1). Each individual’s learning style is deter- * Accepted 20 August 2011. 293 International Journal of Engineering Education Vol. 28, No. 2, pp. 293–301, 2012 0949-149X/91 $3.00+0.00 Printed in Great Britain # 2012 TEMPUS Publications.