(466f) The Impact of Team Composition with Respect to Learning Styles on Team Dynamics in a Chemical Engineering Unit Operations Course

Merriam-Webster’s dictionary gives one definition of team as “two or more draft animals harnessed to the same vehicle or implement.” While we may more traditionally consider another definition, “a number of persons associated together in work or activity,” the first definition better captures the essence of a team. Strapped to the same vehicle, so to speak, team members must work together to progress (move the vehicle). Engineering is a highly team-oriented discipline. Any lack of communication or cooperation among team members can seriously deter the team’s efforts. Education, on the other hand, is focused in large part on the individual: individual learning, individual performance, and individual evaluation. Attempts to include a teamwork aspect in the curriculum are often deterred by students taking the “divide and conquer” method of splitting the work amongst themselves in order to avoid the need to work side-by-side. Some engineering courses have been designed to focus on teamwork as an inherent part of the coursework in an effort to prepare students for the careers they are about to embark upon. Given the opportunity to self-select teams in these courses, students will often form groups that are homogenous with respect to several indicators, including personality and learning style. While homogeneous groups may be preferred for specific tasks, heterogeneous groups can perform better over a more broad range of tasks. Specifically, heterogeneous teams show greater success in tasks that require creativity and innovation, two essential qualities in engineering teams. We are interested in the relationship between team learning style preferences, team performance, and the individual team member evaluation of collective efficacy in the context of a chemical engineering unit operations laboratory course. While the specific execution may differ, this course is a common component among chemical engineering programs and thus offers results we anticipate will be translatable to chemical engineering departments at other universities.

At The Ohio State University, unit operations begins with a weekly lecture during the spring semester that prepares students for the five-week laboratory course in the May term (Maymester).  We are interested in the team dynamics and performance in the Maymester course. Enrollment is consistently 150 to 200 students annually. Teams are assigned by the faculty instructor at the end of the spring term lecture course, based on student preference for experimental track (students may choose from classical, biological, or environmental) and GPA. Teams typically consist of four students that rotate through specific roles (team leader, design engineer, operating engineer, and development engineer), allowing each student to experience each role once.

Our study strives to categorize the team composition with respect to learning styles (homogeneous or heterogeneous) and explore correlations between composition, performance, and team dynamics with no interference in the course. We are using the Felder-Soloman Index of Learning Styles (ILS) to evaluate student learning styles and are interested in separately analyzing each dimension of the related Felder-Silverman learning styles model (active/reflective, sensing/intuitive, visual/verbal, sequential/global) to better understand if team composition with respect to any one dimension is more significant in team dynamics. Team dynamics are evaluated through peer performance evaluations, as well as an individually administered perceptions/collective efficacy survey. The survey includes items such as: group members contributed equally to writing the lab report; I would have preferred to write the lab report myself; I believe in my group’s ability to perform unit operations tasks. By administering this survey with each experiment, we are able to control for variations by unit operation laboratory protocol performed (e.g., distillation column, plug-flow reactor, etc.), as well as identify any changes in team dynamics that may occur as the students become more accustomed to working together.  

Results indicate there is a high deal of variability in team dynamics among the unit operations students. By evaluating the traditionally run laboratory course without intervention (the only items added for study only purposes are the ILS and perception surveys), we are able to establish a baseline for understanding the role of learning styles and influences of team dynamics in our curriculum. An understanding of the variability in performance based on learning style and team composition can help elucidate the complex nature of team dynamics. Armed with such information, instructors can make decisions regarding how to create teams (in courses at all levels) in a manner most beneficial to the students, and students will have a better understanding of teams as they progress through/complete their degrees.