Interactions between humans are greatly impacted by their behavior. These behaviors can be characterized by signals such as smiling, speech, gaze, posture, gesture, etc. Also by the space, surroundings, time, situation, and context created for a particular activity. These signals also define emotion since they are reactions that human beings experience in response to a particular event or situation. Depending on the event or the circumstance, most of these signals can be triggered. That also happens in pedagogical activities in a classroom. Social learning is multi-modal and teaching itself is complex, these underlying cues are not entirely visible and not immediate. We are investigating Context-Aware Classroom (CAC) to provide a multi-modal perception system allowing to capture pedagogical events that occur in it, to help (young) teachers improve their teaching practices. Thanks to deep learning, which has made great progress over the past two decades, and statistical modeling, it is possible to extract and analyze the signals mentioned above to characterize these events. The main problem with this investigation is the fact that the privacy of the participants may not be preserved. From an ethical point of view, a lot of problems can be caused, i.e, privacy must be taken into account when designing artificial intelligence models. Thus, instead of monitoring individual behavior, the focus will be on global emotion, global student engagement, and the global attention level of the whole class using the signals above mentioned.