Investigating grade-level and text genre effects in Quality Talk discussions: An AI-powered discourse analysis of upper primary students’ high-level comprehension

Background: Students in upper primary grades must move beyond basic comprehension toward high-level comprehension (HLC) of text as they read. Small-group, text-based discussions provide opportunities for students to develop their critical analytic thinking and argumentation, supporting their HLC. Aims: We explored the extent to which groups of upper primary students evidenced growth on indicators of HLC as they engaged in small-group, text-based discussions over a school year, while also examining grade-level and text genre differences. Sample: Participants included fourth- (n = 64) and fifth-grade (n = 69) students. Methods: We employed a single-group, longitudinal design, whereby Quality Talk was embedded into the language arts curriculum of six upper elementary classrooms. Video-recorded discussions (n = 371) were transcribed. We employed an artificial intelligence (AI) powered coding approach to identify indicators of HLC in the discussion transcripts. Results: Groups of upper primary students, on average, evidenced growth in the rates of HLC indicators over the school year. Groups composed of fifth-grade students, on average, had higher elaborated explanation rates than fourth-grade students, and all students, on average, produced a higher rate of elaborated explanations for discussions based on mixed genre versus expository genre texts. Conclusions: Findings from this study contribute to a growing body of literature about grade-level differences in upper primary grades, as well as the influence of text genre on indicators of HLC present within small-group discussions. Notably, the study also employed a novel, AI-powered coding approach for our discourse analysis, which warrants further exploration in future research.