Supporting improvements to undergraduate STEM instruction: an emerging model for understanding instructional change teams - Alice Olmstead; Andrea Beach; Charles Henderson (SpringerOpen, 2019)
Abstract Background Instructional change efforts involving teams of change agents are becoming increasingly prevalent at higher education institutions across the US. Teams may be able to make more lasting and high-quality changes to STEM courses than instructors working alone. But team-based change efforts are also risky. They tend to require more resources than individually focused efforts, and there are many ways in which teams can fail to achieve their goals. So far, research on how change leaders can best support instructional change teams has been extremely limited. We lack a context-specific model that can help us to understand how, when, and why instructional change teams are likely to generate positive outcomes. This study begins to address this limitation by exploring the decisions that project leaders make when establishing supports for instructional change teams. Results We use a grounded theory approach to analyze interviews with 28 leaders of team-based instructional change projects and connect our findings to existing literature about teams in other contexts. We identify five categories of key team inputs: the nature of the task, who participates, process constraints, external engagement, and access to resources. We situate these team inputs in a preliminary model of instructional change teams. In our model, we posit that these team inputs are consequential for how teams work together and for how teams think and feel about their work. This in turn leads to various kinds of team outcomes. Conclusions Our current model lays out key decisions that project leaders make when setting up instructional change teams. It also highlights the mechanisms by which instructional change team inputs can lead to various outcomes. We argue that this can guide change leaders to have productive conversations when initiating new teams or troubleshooting collaboration challenges of existing teams. Our research also provides a foundation for deeper exploration into how teams work together and the consequences for team outcomes. In particular, future research focused on the perspectives of team members could test and build on this initial model.