Promoting inclusivity through time-dynamic discourse analysis in digitally-mediated collaborative learning

Abstract

The availability of naturally occurring educational discourse data within educational platforms presents a golden opportunity to make advances in understanding online learner ecologies and enabling new kinds of personalized interventions focused on increasing inclusivity and equity. However, to gain a more substantive view of how peer interaction is influenced by group composition and gender, learning and computational sciences require new automated methodological approaches that will provide a deeper understanding of learners’ communication patterns and interaction dynamics across digitally-meditated group learning platforms. In the current research, we explore learners’ discourse by employing Group Communication Analysis (GCA), a computational linguistics methodology for quantifying and characterizing the discourse sociocognitive processes between learners in online interactions. The aim of this study is to use GCA to investigate the influence of gender and gender pairing on students’ intra- and interpersonal discourse processes in online environments. Students were randomly assigned to one of three groups of varying gender composition: 75% women, 50% women, or 25% women. Our results suggest that the sociocognitive discourse patterns, as captured by the GCA, reveal deeper level patterns in the way individuals interact within online environments along gender and group composition lines. The scalability of the methodology opens the door for future research efforts directed towards understanding, and creating more equitable and inclusive online peer-interactions.