Automatic Recognition of Student Engagement using Deep Learning and Facial Expression
This work addresses the need for intelligent educational interfaces to assess engagement, but it is incremental as it adapts existing deep learning techniques to a specific domain.
The paper tackles the problem of recognizing student engagement from images by developing a deep learning model that pre-trains on facial expression data to overcome data sparsity, achieving improved performance over existing methods on a new dataset with 4627 samples.
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.