A consistent preprocessing pipeline applied to 14 mind wandering datasets reveals varying detection performance across modalities and models, with open code for future work.
Title resolution pending
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
fields
cs.HC 3years
2026 3representative citing papers
Automated acoustic analysis of 36 team-teaching sessions reveals systematic loudness variation differences across teacher experience, student cohorts, and learning task design.
LLM-based multimodal feedback matches educator feedback in learning outcomes but exceeds it in student perceptions of quality, engagement, and reduced cognitive load.
citing papers explorer
-
Automatic Mind Wandering Detection in Educational Settings: A Systematic Review and Multimodal Benchmarking
A consistent preprocessing pipeline applied to 14 mind wandering datasets reveals varying detection performance across modalities and models, with open code for future work.
-
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.
-
LLM-based Multimodal Feedback Produces Equivalent Learning and Better Student Perceptions than Educator Feedback
LLM-based multimodal feedback matches educator feedback in learning outcomes but exceeds it in student perceptions of quality, engagement, and reduced cognitive load.