Online engage-measurement in tutoring session

Abstract

The COVID-19 pandemic has brought about a significant change in the way educa tion is delivered worldwide. Restrictions have forced schools, colleges, and universi ties to hold classes online using video communication services. While this method of teaching has its advantages, one major challenge is determining student engagement during virtual sessions. In traditional classrooms, it is easier to observe student’s interest and engagement through body language and movements. However, this is not the case in an online setting, where monitoring engagement requires more re sources. To address this issue, we have undertaken research to develop a system called ”Online Engage-Measurement” that automates the process of monitoring en gagement by measuring attention and detecting screen sharing. This system will be faster, more efficient, and accessible to educators everywhere. It uses screen sharing detection, face recognition, head position, and eye gaze estimation, as well as an algorithm called ”AttentionEstimator” to determine engagement levels. The system detects the attentiveness of both students and teachers and generates a report for analysis. Besides, our research is unique as this field has not yet been implemented, and our system is the result of our research contributions, which will help us to be a part of the Fourth Industrial Revolution. This initiative has the potential to improve the future of education and solve many problems, such as the development of proctor-less examination system. Utilizing such attention measuring system in online education can provide valuable insights for educators to adapt and refine their teaching methods to align with the needs of their students. It allows for the assessment of the effectiveness of instruction and detection of areas for improvement in student performance, thus providing valuable information to enhance the educa tional experience for students. Thus, the system we have built has the potential to improve student learning experiences and boost tutoring session efficiency.

Description

Cataloged from PDF version of thesis.
Includes bibliographical references (pages 41-42).
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2023.

Keywords

Online engage-measurement, Screen sharing detection, Face recognition, Head pos, Eye gaze estimation, AttentionEstimator, System, Proctor-less, Attentiveness, Unique

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