Term of Award

Spring 2021

Degree Name

Master of Science, Computer Science (M.S.C.S.)

Document Type and Release Option

Thesis (open access)

Copyright Statement / License for Reuse

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.


Department of Computer Science

Committee Chair

Andrew Allen

Committee Member 1

Gursimran Walia

Committee Member 2

Lixin Li


Measuring student engagement has emerged as a significant factor in the process of learning and a good indicator of the knowledge retention capacity of the student. As synchronous online classes have become more prevalent in recent years, gauging a student's attention level is more critical in validating the progress of every student in an online classroom environment. This paper details the study on profiling the student attentiveness to different gradients of engagement level using multiple machine learning models. Results from the high accuracy model and the confidence score obtained from the cloud-based computer vision platform - Amazon Rekognition were then used to statistically validate any correlation between student attentiveness and emotions. This statistical analysis helps to identify the significant emotions that are essential in gauging various engagement levels. This study identified emotions like calm, happy, surprise, and fear are critical in gauging the student's attention level. These findings help in the earlier detection of students with lower attention levels, consequently helping the instructors focus their support and guidance on the students in need, leading to a better online learning environment.

OCLC Number


Research Data and Supplementary Material


Included in

Robotics Commons