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

The paper presents an AI-driven approach to analyze acoustic patterns in team-teaching environments, focusing on 36 recorded sessions involving 12 teachers. By extracting acoustic features such as loudness dynamics, the study reveals that high-experience teachers and collaborative learning tasks lead to greater volume modulation, enhancing classroom engagement and interaction. This research provides insights into the micro-level processes of team teaching, offering a scalable method for practitioners to understand and improve pedagogical strategies in diverse classroom settings.