CHS: Small: Novel Technology Augmented Methods to Improve Team-based Engineering Education for Diverse Teams

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This project will develop an automated system to measure core team processes. Teams are increasingly prevalent in today's workforce, and the ability to work effectively in teams is a competitive advantage. As a result, team-based learning is now widespread in universities across all levels, ranging from first-year engineering design courses to senior capstone projects. The rise of team-based learning has created two inter-related challenges in effectively delivering the optimal benefits of this learning modality. First, while educators have matured in evaluating technical learning, such as quality of results and presentation, they are still in the early stages of understanding, measuring, and educating students on the process of effective teamwork itself. Second, team-based research relies almost exclusively on self-report measures and human observation, which are error prone and problematic. Thus, there is a clear need to augment current methods so scientists and educators can better measure the processes that lead to effective team performance. The project will impact not only team-based education, but behavioral sciences more broadly. By developing methods to objectively measure behavioral correlates of important psychological constructs, behavioral sciences will go beyond the traditional tools for research.Grounded in psychological theory and research, and enabled by advances in engineering, this research will leverage two forms of team interactions to understand key metrics related to individual effectiveness, team dynamics, and the impact of diversity on both individual and team metrics: (1) in-person interactions measured from the audio-visual recordings of team meetings, and (2) work interactions measured from team's shared written online collaborative documents. The system developed through this research will include novel methods to extract action sequences from audio-visual meeting data and from the online documents. Data will be collected in a series of projects across three team environments: (1) targeted studies where teams are formed for 90-minute project sprints; (2) medium-term teams working on a 7-week engineering design internship, and (3) longer-term teams in a semester-long engineering design course. Validated psychological assessments and new metrics will be collected and assembled into an extensive and diverse dataset. A multilayer network model will be developed to represent the different modes of human interaction collected in the individual action sequences. The objective is to extract network features that correlate with meaningful psychological indicators of team dynamics and role emergence within teams.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.