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Engage AI: Leveraging Video Analytics for Instructor-class Awareness in Virtual Classroom Settings

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Conference

2021 ASEE Virtual Annual Conference Content Access

Location

Virtual Conference

Publication Date

July 26, 2021

Start Date

July 26, 2021

End Date

July 19, 2022

Conference Session

Computers in Education 6: Best of CoED

Tagged Division

Computers in Education

Page Count

13

Permanent URL

https://peer.asee.org/37031

Download Count

65

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Paper Authors

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Jeremy Stairs University of Toronto

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Jeremy Stairs is an undergraduate student in computer engineering and artificial intelligence at the University of Toronto (graduating May 2021).

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Raman Mangla University of Toronto

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Raman Mangla is an undergraduate student studying computer engineering with focus on software and artificial intelligence at the University of Toronto (graduating May 2021).

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Manik Chaudhery University of Toronto

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Manik Chaudhery is an undergraduate student studying Computer Engineering at the University Of Toronto with a focus on Artificial Intelligence and Business. Manik will be graduating in May 2021.

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Janpreet Singh Chandhok University of Toronto

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Janpreet Singh Chandhok is an undergraduate student in computer engineering and artificial intelligence at the University of Toronto (graduating May 2021)

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Hamid S. Timorabadi University of Toronto

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Hamid Timorabadi received his B.Sc, M.A.Sc, and Ph.D. degrees in Electrical Engineering from the University of Toronto. He has worked as a project, design, and test engineer as well as a consultant to industry. His research interests include the application of digital signal processing in power systems.

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Abstract

Work in Progress Engage AI: Leveraging video analytics for instructor-class awareness in virtual classroom settings

A difficulty for teachers in COVID-era online teaching settings is assessing engagement and student attention. We developed a system called Engage AI for assessing engagement during live lectures. Engage AI uses video-based machine learning models to detect drowsiness and emotions like happiness and neutrality, and aggregates them in a dashboard that instructors can view as they speak. No video data is transmitted outside of students’ web browsers, and individual students are anonymous to the instructor. Testing in undergraduate engineering lectures resulted in 78.2% of students reporting feeling at least potentially more engaged during the lecture and at least 34.4% reporting feeling more engaged during the lecture. These approaches could be applicable to many forms of remote and in-person education.

Stairs, J., & Mangla, R., & Chaudhery, M., & Chandhok, J. S., & Timorabadi, H. S. (2021, July), Engage AI: Leveraging Video Analytics for Instructor-class Awareness in Virtual Classroom Settings Paper presented at 2021 ASEE Virtual Annual Conference Content Access, Virtual Conference. https://peer.asee.org/37031

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