Automated acoustic analysis of 36 team-teaching sessions reveals systematic loudness variation differences across teacher experience, student cohorts, and learning task design.
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2representative citing papers
The paper adapts prior reflection frameworks into an eight-indicator scheme for software engineering and validates fine-tuned encoder-only transformers that classify student reflections with human-level agreement on most indicators.
citing papers explorer
-
AI-Driven Analytics of Team-Teaching Talk: Acoustic Patterns across Experience, Cohorts and the Learning Design
Automated acoustic analysis of 36 team-teaching sessions reveals systematic loudness variation differences across teacher experience, student cohorts, and learning task design.
-
Identifying Quality Indicators in Student Self-Reflections in Software Engineering
The paper adapts prior reflection frameworks into an eight-indicator scheme for software engineering and validates fine-tuned encoder-only transformers that classify student reflections with human-level agreement on most indicators.