Applying graph theory to examine the dynamics of student discussions in small-group learning

Abstract

Group work in science, technology, engineering, and mathematics courses is an effectivemeans of improving student outcomes, and many different factors can influence the dynamicsof student discussions and, ultimately, the success of collaboration. The substanceand dynamics of group discussions are commonly examined using qualitative methodssuch as discourse analysis. To complement existing work in the literature, we developed aquantitative methodology that uses graph theory to map the progression of talk-turns ofdiscussions within a group. We observed groups of students working with peer facilitatorsto solve problems in biological sciences, with three iterations of data collection and twomajor refinements of graph theory calculations. Results include general behaviors basedon the turns in which different individuals talk and graph theory parameters to quantifygroup characteristics. To demonstrate the potential utility of the methodology, we presentcase studies with distinct patterns: A centralized group in which the peer facilitator behaveslike an authority figure, a decentralized group in which most students talk their fairshare of turns, and a larger group with subgroups that have implications for equity, diversity,and inclusion. Together, these results demonstrate that our adaptation of graph theoryis a viable quantitative methodology to examine group discussions.