Quantifying Student Attention using Convolutional Neural Networks

Andreea Coajă, Cătălin Rusu, Cătălin Rusu

2022

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.

Paper Citation

in Harvard Style

Coajă A. and Rusu C. (2022). Quantifying Student Attention using Convolutional Neural Networks. In Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART, ISBN 978-989-758-547-0, pages 293-299. DOI: 10.5220/0010816500003116

in Bibtex Style

@conference{icaart22,
author={Andreea Coajă and Cătălin Rusu},
title={Quantifying Student Attention using Convolutional Neural Networks},
booktitle={Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART,},
year={2022},
pages={293-299},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010816500003116},
isbn={978-989-758-547-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART,
TI - Quantifying Student Attention using Convolutional Neural Networks
SN - 978-989-758-547-0
AU - Coajă A.
AU - Rusu C.
PY - 2022
SP - 293
EP - 299
DO - 10.5220/0010816500003116