  To perform contingent teaching and be responsive to students' needs during
class, lecturers must be able to quickly assess the state of their audience.
While effective teachers are able to gauge easily the affective state of the
students, as class sizes grow this becomes increasingly difficult and less
precise. The Wits Intelligent Teaching System (WITS) aims to assist lecturers
with real-time feedback regarding student affect. The focus is primarily on
recognising engagement or lack thereof. Student engagement is labelled based on
behaviour and postures that are common to classroom settings. These proxies are
then used in an observational checklist to construct a dataset of engagement
upon which a CNN based on AlexNet is successfully trained and which
significantly outperforms a Support Vector Machine approach. The deep learning
approach provides satisfactory results on a challenging, real-world dataset
with significant occlusion, lighting and resolution constraints.
