  In this work we present a multi-modal machine learning-based system, which we
call ACORN, to analyze videos of school classrooms for the Positive Climate
(PC) and Negative Climate (NC) dimensions of the CLASS observation protocol
that is widely used in educational research. ACORN uses convolutional neural
networks to analyze spectral audio features, the faces of teachers and
students, and the pixels of each image frame, and then integrates this
information over time using Temporal Convolutional Networks. The audiovisual
ACORN's PC and NC predictions have Pearson correlations of $0.55$ and $0.63$
with ground-truth scores provided by expert CLASS coders on the UVA Toddler
dataset (cross-validation on $n=300$ 15-min video segments), and a purely
auditory ACORN predicts PC and NC with correlations of $0.36$ and $0.41$ on the
MET dataset (test set of $n=2000$ videos segments). These numbers are similar
to inter-coder reliability of human coders. Finally, using Graph Convolutional
Networks we make early strides (AUC=$0.70$) toward predicting the specific
moments (45-90sec clips) when the PC is particularly weak/strong. Our findings
inform the design of automatic classroom observation and also more general
video activity recognition and summary recognition systems.
