  Given that approximately half of science, technology, engineering, and
mathematics (STEM) undergraduate students in U.S. colleges and universities
leave by the end of the first year [15], it is crucial to improve the quality
of classroom environments. This study focuses on monitoring students' emotions
in the classroom as an indicator of their engagement and proposes an approach
to address this issue. The impact of different facial parts on the performance
of an emotional recognition model is evaluated through experimentation. To test
the proposed model under partial occlusion, an artificially occluded dataset is
introduced. The novelty of this work lies in the proposal of an occlusion-aware
architecture for facial action units (AUs) extraction, which employs attention
mechanism and adaptive feature learning. The AUs can be used later to classify
facial expressions in classroom settings.
  This research paper's findings provide valuable insights into handling
occlusion in analyzing facial images for emotional engagement analysis. The
proposed experiments demonstrate the significance of considering occlusion and
enhancing the reliability of facial analysis models in classroom environments.
These findings can also be extended to other settings where occlusions are
prevalent.
