  In this paper, we propose a novel technique for measuring behavioral
engagement through students' actions recognition. The proposed approach
recognizes student actions then predicts the student behavioral engagement
level. For student action recognition, we use human skeletons to model student
postures and upper body movements. To learn the dynamics of student upper body,
a 3D-CNN model is used. The trained 3D-CNN model is used to recognize actions
within every 2minute video segment then these actions are used to build a
histogram of actions which encodes the student actions and their frequencies.
This histogram is utilized as an input to SVM classifier to classify whether
the student is engaged or disengaged. To evaluate the proposed framework, we
build a dataset consisting of 1414 2-minute video segments annotated with 13
actions and 112 video segments annotated with two engagement levels.
Experimental results indicate that student actions can be recognized with top 1
accuracy 83.63% and the proposed framework can capture the average engagement
of the class.
