Overcoming Barriers to Heterogeneous-Group Learning in the Political Science Classroom

Despite positive fi ndings, small-group activities continue to lag behind lectures in political science classrooms. This article argues that one barrier to wider adoption of more innovative activities is uncertainty about how to effi ciently and fairly create teams that each are heterogeneous and as a set are balanced across relevant characteristics. We fi rst describe recent fi ndings and strategies for creating teams; we then detail our concrete, general approach for incorporating several student characteristics into team creation. We then describe implementations of this approach using freely available software in two undergraduate political science courses—one in American politics and one in political methodology. In these applications and in a variety of simulated data, we demonstrate that teams created using our method are better balanced than those created by randomly allocating students to teams. Recent educational research argues that students learn more during class time that is spent working actively rather than listening more passively to an instructor-delivered lecture. However, despite these positive fi ndings, political science classrooms tend to rely on teacher-centered rather than student-centered activities that promote active learning (Archer and Miller 2011 ). In our experience, this is partly due to instructors’ uncertainty about how to form active-learning teams in ways that are fair, fast, and generate balance on student characteristics across the groups. Heterogeneity within groups has been found to improve student outcomes, and it enables each team to draw on a more diverse set of skills, experiences, and knowledge from its members. Here, we seek to lower instructors’ costs of implementing group activities, enabling them to focus on structuring the content of activities with confi dence that their procedure scales to large classes, large groups, or classes that change rapidly as students join or drop out during the term. We accomplish this by deploying research tools that already may be familiar to many instructors and that allow us to incorporate substantial information about students. For those unfamiliar with our approach, this article describes the necessary steps and off ers supplementary support. Our method for quickly creating heterogeneous groups easily can incorporate almost as many student attributes as there are students. Below, we describe recent pedagogical fi ndings about smallgroup learning and detail common strategies for creating such groups. We then introduce our approach in the context of an actual project that we assigned in class, an exercise applying a policymaking model (Kingdon 2003 ) to the Patient Protection and Aff ordable Care Act (ACA) legislation in a medium-sized undergraduate course in American politics. Here, we explicate the three lines of R code that create heterogeneous groups. We then demonstrate that our approach generates teams that are considerably more heterogeneous than if we allocate students purely at random—but with virtually no additional work on the part of the instructor. Our approach generates well-balanced allocations across teams regardless of whether we consider a single discrete measure (e.g., student gender or semester of enrollment) or a larger set of measures with a variety of distributional forms. Ultimately, a clear, scalable, and successful procedure will allow instructors to confi dently design more team-based work for political science classes of all sizes. TEAM-BASED LEARNING AND ITS CHALLENGES When compared to lectures, small projects that involve active student teams increase test scores in political science (Centellas and