Analysis of Attention Span of Students using Deep Learning

This research work presents an experimental work of the Analysis of Attention Span of Students using Deep Learning, a novel application employing deep learning techniques for assessing student engagement in educational settings. The system incorporates facial recognition, eye tracking, and head pose...

Full description

Saved in:

Bibliographic Details
Published in:	2024 MIT Art, Design and Technology School of Computing International Conference (MITADTSoCiCon) pp. 1 - 7
Main Authors:	Warankar, Varad, Jain, Nishtha, Patil, Bhavesh, Faizaan, Mohammed, Jagdale, Balaso, Sugave, Shounak
Format:	Conference Proceeding
Language:	English
Published:	IEEE 25-04-2024
Subjects:	Accuracy Artificial Intelligence Attention Deep learning Educational technology Eye Gaze Face Recognition Head Position Real-time systems Solid modeling Solids User interfaces
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Description
Summary:	This research work presents an experimental work of the Analysis of Attention Span of Students using Deep Learning, a novel application employing deep learning techniques for assessing student engagement in educational settings. The system incorporates facial recognition, eye tracking, and head pose analysis to offer real-time insights into students' attentiveness during lessons.The introduction outlines the motivation behind the research, emphasizing its significance in the dynamic landscape of education. The report addresses the project's relevance in optimizing learning environments, delivering personalized education, and addressing challenges in remote learning scenarios.The implementation overview details the integration of key components and the calculation of real-time metrics related to student attention. Practical applications of the system are discussed, highlighting its role in educational adaptability, early intervention, and the generation of data-driven insights for continuous improvement in teaching methodologies. The conclusion summarizes key findings and discusses the potential implications of the Analysis of Attention Span of Students using Deep Learning. This research work aims to contribute to the discourse surrounding educational technology, emphasizing adaptability and personalization for an enriched learning experience and accuracy is measured. Moreover, end user interface is implemented, and impact is analyzed.
DOI:	10.1109/MITADTSoCiCon60330.2024.10575321