The Wits Intelligent Teaching System: Detecting Student Engagement During Lectures Using Convolutional Neural Networks

TL;DR

The paper presents WITS, a CNN-based system that detects student engagement in real-time during lectures, addressing challenges of large class sizes and environmental variability.

Contribution

It introduces a novel CNN model trained on a new dataset for classroom engagement detection, outperforming traditional SVM methods.

Findings

CNN significantly outperforms SVM in engagement detection

System performs well despite occlusion and lighting challenges

Real-time feedback capability enhances teaching responsiveness

Abstract

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.