Quantifying Student Attention using Convolutional Neural Networks

Andreea Coajă; Cătălin V. Rusu; Cătălin V. Rusu

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Paper

Quantifying Student Attention using Convolutional Neural Networks

Topics: Machine Learning; Neural Networks; Soft Computing

In Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART, 293-299, 2022

Authors: Andreea Coajă ¹ and Cătălin V. Rusu ^{1

;

2}

Affiliations: ¹ Department of Computer-Science, Babeş-Bolyai University, Romania ; ² Institute for German Studies, Babeş-Bolyai University, Romania

Keyword(s): Machine Learning, Prediction, Image Recognition, Classification.

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.

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Paper citation in several formats:

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; ISSN 2184-433X, SciTePress, pages 293-299. DOI: 10.5220/0010816500003116

@conference{icaart22,
author={Andreea Coajă. and Cătălin V. 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},
issn={2184-433X},
}

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
IS - 2184-433X
AU - Coajă, A.
AU - Rusu, C.
PY - 2022
SP - 293
EP - 299
DO - 10.5220/0010816500003116
PB - SciTePress