Using Student Action Recognition to Enhance the Efficiency of Tele-education

Eleni Dimitriadou, Andreas Lanitis, Andreas Lanitis

2022

Abstract

Due to the COVID-19 pandemic, many schools worldwide are using tele-education for class delivery. However, this causes a problem related to students’ active class participation. We propose to address the problem with a system that recognizes student’s actions and informs the teacher accordingly, while preserving the privacy of students. In the proposed action recognition system, seven typical actions performed by students attending online courses, are recognized using Convolutional Neural Network (CNN) architectures. The actions considered were defined by considering the relevant literature and educator’s views, and ensure that they provide information about the physical presence, active participation, and distraction of students, that constitute important pedagogical aspects of class delivery. The action recognition process is performed locally on the device of each student, thus it is imperative to use classification methods that require minimal computational load and memory requirements. Initial experimental results indicate that the proposed action recognition system provides promising classification results, when dealing with new instances of previously enrolled students or when dealing with previously unseen students.

Paper Citation

in Harvard Style

Dimitriadou E. and Lanitis A. (2022). Using Student Action Recognition to Enhance the Efficiency of Tele-education. In Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 5: VISAPP; ISBN 978-989-758-555-5, SciTePress, pages 543-549. DOI: 10.5220/0010868200003124

in Bibtex Style

@conference{visapp22,
author={Eleni Dimitriadou and Andreas Lanitis},
title={Using Student Action Recognition to Enhance the Efficiency of Tele-education},
booktitle={Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 5: VISAPP},
year={2022},
pages={543-549},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010868200003124},
isbn={978-989-758-555-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 5: VISAPP
TI - Using Student Action Recognition to Enhance the Efficiency of Tele-education
SN - 978-989-758-555-5
AU - Dimitriadou E.
AU - Lanitis A.
PY - 2022
SP - 543
EP - 549
DO - 10.5220/0010868200003124
PB - SciTePress