The Wits Intelligent Teaching System: Detecting Student Engagement During Lectures Using Convolutional Neural Networks
This work addresses the challenge for lecturers in large classes to assess student engagement in real-time, though it is incremental as it applies an existing CNN architecture to a new educational domain.
The paper tackled the problem of automatically detecting student engagement during lectures by training a convolutional neural network (CNN) based on AlexNet on a dataset labeled with behavioral and postural proxies, which significantly outperformed a Support Vector Machine approach on a challenging real-world dataset with occlusion, lighting, and resolution constraints.
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.