EXECUTE: Exploring Eye Tracking to Support E-learning (original) (raw)

Khan, Ahsan Raza, Khosravi, Sara, Hussain, Sajjad ORCID logoORCID: https://orcid.org/0000-0003-1802-9728, Ghannam, Rami ORCID logoORCID: https://orcid.org/0000-0001-6910-9280, Zoha, Ahmed ORCID logoORCID: https://orcid.org/0000-0001-7497-9336 and Imran, Muhammad Ali ORCID logoORCID: https://orcid.org/0000-0003-4743-9136(2021) EXECUTE: Exploring Eye Tracking to Support E-learning. In: IEEE Global Engineering Education Conference (EDUCON2022), Tunis, Tunisia, 28-31 March 2022, pp. 670-676. ISBN 9781665444347(doi: 10.1109/EDUCON52537.2022.9766506)

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Abstract

The outbreak of the COVID-19 pandemic has caused unprecedented disruption to education and progressed remote teaching as a predominant model for delivering educational content. However, the online teaching and learning model has its challenges, such as the lack of technological tools to quantity the student attention and engagement with the learning content. This paper focuses on developing an e-learning framework for capturing and analysing the students’ attention during remote teaching sessions and subsequently profiling their learning behaviour leveraging eye-tracking data. Our proposed eye-tracking solution deploys a webcam to capture and track raw gaze points that grant the user the freedom of natural head movement and scalability compared to conventional eye-tracking approaches. We derived various gaze metrics in conjunction with state-of the-art machine learning (ML) models like logistic regression, support vector machine and polynomial regression to classify the student attention with an accuracy above 91%. Furthermore, our findings can help in the early detection and diagnosis of attention deficit hyperactivity disorder (ADHD) among students, thus supporting their learning journeys by creating an adaptive learning environment tailored to their needs.

Item Type: Conference Proceedings
Status: Published
Refereed: Yes
Glasgow Author(s) Enlighten ID: Zoha, Dr Ahmed and Khosravi, Sara and Khan, Ahsan Raza and Imran, Professor Muhammad and Hussain, Professor Sajjad and Ghannam, Professor Rami
Authors: Khan, A. R., Khosravi, S., Hussain, S., Ghannam, R., Zoha, A., and Imran, M. A.
College/School: College of Science and Engineering > School of Engineering > Autonomous Systems and ConnectivityCollege of Science and Engineering > School of Engineering > Electronics and Nanoscale Engineering
ISBN: 9781665444347
Published Online: 11 May 2022
Copyright Holders: Copyright © 2021 IEEE
First Published: First published in IEEE Global Engineering Education Conference (EDUCON2022): 670-676
Publisher Policy: Reproduced in accordance with the publisher copyright policy
Related URLs: OrganisationPublisher

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Deposit and Record Details

ID Code: 258669
Depositing User: Dr Aniko Szilagyi
Datestamp: 09 Nov 2021 10:02
Last Modified: 11 Apr 2025 05:47
Date of acceptance: 5 December 2021
Date of first online publication: 11 May 2022
Date Deposited: 9 November 2021
Data Availability Statement: No