Body language is an essential source of information in everyday communication. Low signal-to-noise ratio prevents us from using it in the automatic processing of student behaviour, an obstacle that we are slowly overcoming with advanced statistical methods. Instead of profiling individual behaviour of students in the classroom, the idea is to compare students and connect the observed traits to different levels of attention. With the usage of novel techniques from the field of computer vision, we focus on features that can be automatically extracted with a system of cameras, by means of passive observation of the classroom population. We show parallels between our work and previous theories and formulate a new concept for measuring the level of attention based on synchronization of student body movement. We observed that students with lower levels of attention are slower to react than focused students, a phenomenon we named "sleepers' lag." This realization may give rise to novel measurements that can act as a technological support for teacher metacognition. The goal is to improve the teacher-student conversation and to propose techniques that can enable a shorter feedback loop of the teacher's performance compared to the current-day methods.