Automatic Recognition of Student Engagement using Deep Learning and Facial Expression

TL;DR

This paper introduces a deep learning approach for recognizing student engagement from facial images, leveraging pre-training on facial expressions to address data scarcity and improve accuracy.

Contribution

The study proposes a novel two-step deep learning framework that pre-trains on facial expressions and then fine-tunes on engagement data, enhancing recognition performance.

Findings

The engagement recognition model outperforms traditional methods like HOG and SVM.

Pre-training on facial expressions improves engagement detection accuracy.

The dataset contains 4627 labeled engagement samples.

Abstract

Engagement is a key indicator of the quality of learning experience, and one that plays a major role in developing intelligent educational interfaces. Any such interface requires the ability to recognise the level of engagement in order to respond appropriately; however, there is very little existing data to learn from, and new data is expensive and difficult to acquire. This paper presents a deep learning model to improve engagement recognition from images that overcomes the data sparsity challenge by pre-training on readily available basic facial expression data, before training on specialised engagement data. In the first of two steps, a facial expression recognition model is trained to provide a rich face representation using deep learning. In the second step, we use the model's weights to initialize our deep learning based model to recognize engagement; we term this the engagement model. We train the model on our new engagement recognition dataset with 4627 engaged and disengaged samples. We find that the engagement model outperforms effective deep learning architectures that we apply for the first time to engagement recognition, as well as approaches using histogram of oriented gradients and support vector machines.