Solving today's most challenging societal problems requires innovative breakthroughs that transcend individual disciplines and deeper knowledge integration. However, achieving this through teamwork is challenging due to the lack of adequate training. To address this, methods from allied disciplines need to be adapted for training transdisciplinary researchers. This presentation discusses team science training focused on coastal resilience. It brings together a multidisciplinary team of scholars focused on improving problem solving and teamwork in science.

First, a team of faculty coaches was recruited to guide a class of diverse graduate students from varied disciplines related to the problem of coastal resilience. Second, a series of workshops was developed to train students on the fundamentals of team science as well as transdisciplinary knowledge building. Third, to foster individual and team learning, an intervention focusing on reflection in teamwork processes was used to ensure students monitor both the task of transdisciplinary problem solving, as well as the teamwork processes engaged while collaborating.

Graduate students were introduced to the principles of team science, collaborative problem solving, and effective self-reflective tools and strategies. Additionally, students gained experience working with coastal community partners (e.g., municipalities, NGOs). As such, this community-based climate-resilience project enabled students to practice team science research and use reflective practices to improve their competencies with various stakeholders. Assessment of team processes, along with reflections on teamwork and taskwork, were used to highlight areas of collaboration needing improvement. For this presentation we describe our analyses of the team reflections across the workshops. These reflections were based on prompts designed to helps students consider their experience in teamwork and in taskwork and what they found challenging. Additionally, they reflected on what they learned about interdisciplinarity teamwork and taskwork.

We conducted complementary quantitative and qualitiative computational analyses in order to understand how reflections on team science varied across time and teams. For the quantitative analyses we conducted topic modeling of the text from the reflections. Topic modeling is a computational approach for natural language processing. It compares texts to detect related words and phrases and clusters them to help interpret the document contents. For the qualitative analyses we analyzed the social, cognitive, and emotional content of the reflections. Reflections from the students were aggregated based upon the workshop (first or last), and the team on which they participated. These texts were then analyzed to identify patterns and themes capable of discerning the nature of the reflections and how they varied depending on the context. We first discuss differences in quantity of reflections and how this varied by the start and end of the team training and depending on the focus of the reflections. We then describe changes in structure of themes based upon topic modeling and how this varied by time and team. We next discuss the socio-emotional content of the reflections depending on time in training and team. We conclude with a discussion of these and related findings and how the project evolved to better address the team science training needs of the graduate students.