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In this work, we propose a novel method for assessing students’ behavioral engagement by representing student’s actions and their frequencies over an arbitrary time interval as a histogram of actions. This histogram and the student’s gaze are utilized as input to a classifier that determines whether the student is engaged or not. For action recognition, we use students’ skeletons to model their postures and upper body movements. To learn the dynamics of a student’s upper body, a 3D-CNN model is developed. The trained 3D-CNN model recognizes actions within every 2-minute video segment then these actions are used to build the histogram of actions. To evaluate the proposed framework, we build a dataset consisting of 1414 video segments annotated with 13 actions and 963 2-minute video segments annotated with two engagement levels. Experimental results indicate that student actions can be recognized with top-1 accuracy 86.32% and the proposed framework can capture the average engagement of the class with a 90% F1-score.