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. 10.18260/1-2--37031

\bibitem{asee_peer_37031}
  Stairs, J., \& Mangla, R., \& Chaudhery, M., \& Chandhok, J. S., \& Timorabadi, H. S. (2021, July), \emph{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.
  10.18260/1-2--37031

Jeremy Stairs, Raman Mangla, Manik Chaudhery, Janpreet Singh Chandhok, and Hamid S. Timorabadi.  "Engage AI: Leveraging Video Analytics for Instructor-class Awareness in Virtual Classroom Settings".  2021 ASEE Virtual Annual Conference Content Access, Virtual Conference, 2021, July.  ASEE Conferences, 2021.  https://peer.asee.org/37031 Internet.  22 Dec, 2024

\bibitem{asee_peer_37031}
  Jeremy Stairs, Raman Mangla, Manik Chaudhery, Janpreet Singh Chandhok, and Hamid S. Timorabadi.
  "Engage AI: Leveraging Video Analytics for Instructor-class Awareness in Virtual Classroom Settings".
  \emph{2021 ASEE Virtual Annual Conference Content Access, Virtual Conference, 2021, July}.
  ASEE Conferences, 2021.
  https://peer.asee.org/37031 Internet.  22 Dec, 2024

@INPROCEEDINGS{asee_peer_37031
author = "Jeremy Stairs, Raman Mangla, Manik Chaudhery, Janpreet Singh Chandhok, and Hamid S. Timorabadi"
title = "Engage AI: Leveraging Video Analytics for Instructor-class Awareness in Virtual Classroom Settings"
booktitle = "2021 ASEE Virtual Annual Conference Content Access"
year = "2021"
month = "July"
address = "Virtual Conference"
publisher = "ASEE Conferences"
note = {https://peer.asee.org/37031}
number = {10.18260/1-2--37031}
}

TY  - CPAPER
AB  - 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.
AU  - Jeremy Stairs
AU  - Raman Mangla
AU  - Manik Chaudhery
AU  - Janpreet Singh Chandhok
AU  - Hamid S. Timorabadi
CY  - Virtual Conference
DA  - 2021/07/26
PB  - ASEE Conferences
TI  - Engage AI: Leveraging Video Analytics for Instructor-class Awareness in Virtual Classroom Settings
UR  - https://peer.asee.org/37031
DO  - 10.18260/1-2--37031
ER  -