Quantifying Student Attention using Convolutional Neural Networks

Abstract

In this study we propose a method for quantifying student attention based on Gabor filters, a convolutional neural network and a support vector machine (SVM). The first stage uses a Gabor filter, which extracts intrinsic facial features. The convolutional neural network processes this initial transformation and in the last layer a SVM performs the classification. For this task we have constructed a custom dataset of images. The dataset consists of images from the Karolinska Directed Emotional Faces dataset, from actual high school online classes and from volunteers. Our model showed higher accuracy when compared to other convolutional models such as AlexNet and GoogLeNet.