![]() ![]() The effectiveness of discussions depends on how the members of a group interact with one another, and many factors can influence group dynamics ( Figure 1, left). Citations are available in the body of the text. ![]() In this study, we adapt graph theory methodologies to examine the dynamics of these discussions. In the existing literature, quality of small-group discussions is typically analyzed by discourse analysis. The dynamics and quality of these discussions can affect student outcomes, such as cognitive learning, development of process skills, affect, and persistence. Student discussions can be influenced by a number of factors, including group composition, sense of belonging, and values and behaviors related to collaborative activities. Small-group discussions in STEM learning. Empirically, group discussions help students develop cognitive skills such as critical thinking ( Webb, 1982b Gokhale, 1995 Bligh, 2000), problem solving ( Heller et al., 1992), and disciplinary understanding ( Freeman et al., 2014) enhance important skills such as communication ( Webb and Farivar, 1994) and metacognition ( Webb and Mastergeorge, 2003 Veenman et al., 2006 Bromme et al., 2010) improve affect such as interest and motivation ( Skinner and Belmont, 1993 Ryan, 2000) and increase completion rates in courses and persistence in STEM majors ( Tinto, 1997 Freeman et al., 2014 Loes et al., 2017 Figure 1, right).įIGURE 1. Learning theories such as constructivism provide broad explanations for the theoretical basis of group discussions ( National Research Council, 2000 Chi, 2009 Chi and Wylie, 2014). Together, these results demonstrate that our adaptation of graph theory is a viable quantitative methodology to examine group discussions.Ĭollaboration and small-group discussions form the foundation for many evidence-based instructional practices and are effective means of enhancing student learning in science, technology, engineering, and mathematics (STEM). ![]() To demonstrate the potential utility of the methodology, we present case studies with distinct patterns: a centralized group in which the peer facilitator behaves like an authority figure, a decentralized group in which most students talk their fair share of turns, and a larger group with subgroups that have implications for equity, diversity, and inclusion. Results include general behaviors based on the turns in which different individuals talk and graph theory parameters to quantify group characteristics. We observed groups of students working with peer facilitators to solve problems in biological sciences, with three iterations of data collection and two major refinements of graph theory calculations. To complement existing work in the literature, we developed a quantitative methodology that uses graph theory to map the progression of talk-turns of discussions within a group. The substance and dynamics of group discussions are commonly examined using qualitative methods such as discourse analysis. Group work in science, technology, engineering, and mathematics courses is an effective means of improving student outcomes, and many different factors can influence the dynamics of student discussions and, ultimately, the success of collaboration. ![]()
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