  Student engagement is a key construct for learning and teaching. While most
of the literature explored the student engagement analysis on computer-based
settings, this paper extends that focus to classroom instruction. To best
examine student visual engagement in the classroom, we conducted a study
utilizing the audiovisual recordings of classes at a secondary school over one
and a half month's time, acquired continuous engagement labeling per student
(N=15) in repeated sessions, and explored computer vision methods to classify
engagement levels from faces in the classroom. We trained deep embeddings for
attentional and emotional features, training Attention-Net for head pose
estimation and Affect-Net for facial expression recognition. We additionally
trained different engagement classifiers, consisting of Support Vector
Machines, Random Forest, Multilayer Perceptron, and Long Short-Term Memory, for
both features. The best performing engagement classifiers achieved AUCs of .620
and .720 in Grades 8 and 12, respectively. We further investigated fusion
strategies and found score-level fusion either improves the engagement
classifiers or is on par with the best performing modality. We also
investigated the effect of personalization and found that using only 60-seconds
of person-specific data selected by margin uncertainty of the base classifier
yielded an average AUC improvement of .084. 4.Our main aim with this work is to
provide the technical means to facilitate the manual data analysis of classroom
videos in research on teaching quality and in the context of teacher training.
