Collaborative Research: Interpersonal Coordination and Coregulation during Collaborative Problem Solving

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Collaborative problem solving (CPS) is an essential skill in our increasingly connected and globalized world. Yet, there is a paucity of knowledge on how to define, measure, and develop this skill, especially in the context of STEM learning. A team of investigators from Notre Dame University, Florida State University, and Arizona State University will seek to discover how interpersonal interactions arise and influence CPS processes and outcomes in digital STEM learning environments. The research will focus on groups of high school and college students collaborating virtually within a STEM educational game called Physics Playground. The hypothesis is that CPS effectiveness can be improved by providing real-time automated feedback on the ongoing collaboration. The investigators will collect an array of data, ranging from individual physiological measures to learning outcome measures to group communication patterns. Their goal is to improve the design of future CPS learning environments, making them more enjoyable, engaging, and effective. The project is funded by the EHR Core Research (ECR) program, which supports work that advances the fundamental research literature on STEM learning.The research will involve dyads and triads collaborating virtually within a learning environment called Physics Playground, a STEM educational game. Data will be collected in both the lab and classroom, using a diverse sample of high school and college students from three sites across the U.S. The team will integrate data from a range of sources including modeling of small group problem solving and collaborative learning and from the modeling of low-level data from eye tracking, facial feature tracking, psychophysiology, and linguistic/paralinguistic speech analysis. Additionally, the team will study the moderating effects of task constraints and group composition on CPS processes and outcomes. A further goal is to model dynamic CPS processes using nonlinear time series analyses and multimodal deep recurrent neural networks. The computational models will be integrated into the learning environment to test the hypothesis that CPS outcomes can be improved by providing automated feedback on unfolding collaborative processes. Thus, by blending basic experimental research, multisensor-multimodal analysis, computational modeling, and dynamic computerized intervention, the researchers will attempt to make foundational theoretical, methodological, and technological advances.