Virtual Teaching Assistant for Capturing Facial and Pose Landmarks of the Students in the Classroom Using Deep Learning

Roznim Mohamad Rasli

QR Code Link :
Type :	article
ISSN :	1548-3673
Main Author :	Roznim Mohamad Rasli
Title :	Virtual Teaching Assistant for Capturing Facial and Pose Landmarks of the Students in the Classroom Using Deep Learning

Place of Production :	Tanjung Malim
Publisher :	Fakulti Komputeran dan Meta Teknologi
Year of Publication :	2023
Notes :	International Journal of e-Collaboration
Corporate Name :	Universiti Pendidikan Sultan Idris
HTTP Link :	Click to view web link

Abstract : Universiti Pendidikan Sultan Idris

This research focuses on the learning challenges that both students and teachers face during the learning process. It addresses the different techniques and methods used for face recognition. The proposed VTA model uses the convolutional neural networks to recognize the identities of the student. It gathers the facial expressions and body poses of each student in the classroom and predicts the attention level of that student, thus determining his/her learning capabilities. This research will help the students achieve their learning objectives by being able to get an accurate and real evaluation of their contribution and attention during the classes. Also, the proposed VTA model helps the teacher get some insight into his/her teaching methodologies during the class as the model will observe and record the attentiveness of the students. This research will have a significant positive impact on student success and on effective lecturing. 2023 IGI Global. All rights reserved.

This material may be protected under Copyright Act which governs the making of photocopies or reproductions of copyrighted materials.
You may use the digitized material for private study, scholarship, or research.

Back to previous page