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"’We're looking good’: Social exchange and regulation temporality in collaborative design”

2/5/2021

 
Third-year doctoral student Ha Nguyen (right) is first author of an article in Learning and Instruction analyzing collaboration patterns and learning outcomes among four teams of undergraduate engineering students.
 
The title of the article is “’We're looking good’: Social exchange and regulation temporality in collaborative design.”
 
Co-authors are Associate Professor Kyu Yon Lim (Ewha Women’s University), Lily Wu (UCI Engineering), Assistant Professor Christian Fisher (University of Tuebingen), and Professor Mark Warschauer.
 
Nguyen is specializing in Teaching, Learning, and Educational Improvement (TLEI) for her doctoral work. Her research interests include design of STEM learning experiences and multimodal assessment to study collaboration and conceptual understanding. She is advised by Warschauer and Professor Rossella Santagata.

​Abstract
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​Collaborative tasks do not always promote equal learning. Varying levels of social interactions and regulation at the individual and group levels can influence knowledge construction efforts and learning success. To understand which collaboration patterns may be more conducive to learning, this study examined the relation between social exchange, regulation, and learning outcomes. Four project-based engineering undergraduate teams were audiotaped in collaborative tasks (7514 talk turns). Discourse was coded for regulation processes and types (self and socially shared regulation), and analyzed with Epistemic Network Analysis and Process Mining. We find that teams who reported more frequent social exchange engaged in shared regulation together with planning and monitoring more frequently, while teams with less exchange engaged in long durations of collaboration. Furthermore, students in teams with more engaged regulation reported enhanced beliefs in group efficacy to solve collaborative tasks. The study illustrates the potential of applying quantitative approaches to analyzing rich discourse.
 

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