AI-Driven Analytics of Team-Teaching Talk: Acoustic Patterns across Experience, Cohorts and the Learning Design

2026-04-19

AI Summary

This study addresses a critical gap in the empirical understanding of the micro-level verbal interactions among teachers in team-teaching contexts, particularly with respect to variations linked to teacher experience, student population, and task design. Drawing on spatial pedagogy theory, this work pioneers the application of AI-driven speech signal processing to multi-teacher classroom settings, automatically extracting acoustic features such as voice quality, intonation, and loudness, and integrating them with coded teaching behaviors for multidimensional analysis. This approach overcomes the limitations of traditional methods reliant on manual transcription or small-scale observation, enabling scalable investigation of team-teaching dynamics. Findings reveal that loudness variation is significantly more pronounced among experienced teachers, in undergraduate classrooms, and during collaborative tasks, suggesting a strategic use of vocal modulation to emphasize key content and foster interaction.

Abstract

As classroom cohorts expand, team teaching is increasingly used to integrate the expertise and pedagogical perspectives of multiple teachers. Yet, there is limited empirical understanding of how team teaching unfolds in practice, particularly regarding differences in teachers'contributions across experience levels, student cohorts, and learning task design. Prior research on team teaching has largely relied on retrospective self-reports or small-scale observations, offering limited insight into the micro-level processes through which team teaching is enacted. Teacher talk offers a scalable lens on these processes. While research in individual teaching contexts shows that acoustic features of speech (e.g., voice quality, intonation, and loudness) can shape student learning, evidence from team-teaching settings remains scarce. Moreover, capturing such features through manual observation or transcription is especially challenging in team-teaching classrooms, where multiple teachers speak across extended sessions and spatial locations, limiting scalability without automation. Grounded in spatial pedagogy theory and team-teaching research, this paper presents an AI-based speech processing approach to analyse classroom talk in team-teaching settings. We analysed 36 recorded undergraduate and postgraduate sessions involving 12 teachers. Spatial pedagogy behaviours were coded and acoustic features extracted to examine variation across teachers'experience, student cohorts, and the learning task design. The results reveal systematic differences, most notably in loudness dynamics: high-experience teachers, undergraduate classes and collaborative learning tasks exhibited greater loudness variation, suggesting more frequent modulation of volume to foreground key information and support classroom interaction and engagement.

Problem

• team teaching
• teacher talk
• acoustic features
• classroom interaction
• learning design

Innovation

• AI-driven speech analysis
• team teaching
• acoustic features
• spatial pedagogy
• loudness dynamics