Term of Award
Spring 2020
Degree Name
Master of Science in Computer Science (M.S.)
Document Type and Release Option
Thesis (restricted to Georgia Southern)
Copyright Statement / License for Reuse
This work is licensed under a Creative Commons Attribution 4.0 License.
Department
Department of Computer Science
Committee Chair
Andrew Allen
Committee Member 1
Pradipta De
Committee Member 2
Mehdi Allahyari
Abstract
Teachers use observational cues in the classroom to identify student attentiveness. However, effectiveness of this technique decreases with increasing class size. This work presents an approach for automating these observational cues from the students’ facial expressions and identifying their attentiveness via a neural network machine learning model. Results of the deep learning Convolutional Neural Network model were compared with the range of confidence values obtained from a cloud-based emotion recognition service to perform statistical analysis. The statistical analysis method consisted of correlation analysis which provided the association between the attentiveness level and facial emotions and regression analysis which helped find out the most significant facial emotions. Videos of students were collected during classes and dataset was created for attentive and inattentive students for training the machine learning model. This system can be highly useful for teachers for early identification of inattentive students and taking necessary actions to enhance student learning.
OCLC Number
1158625016
Catalog Permalink
https://galileo-georgiasouthern.primo.exlibrisgroup.com/permalink/01GALI_GASOUTH/1r4bu70/alma9916348593302950
Recommended Citation
Tabassum, Tasnia, "Non-Intrusive Identification of Student Attentiveness and Finding Their Correlation with Detectable Facial Emotions" (2020). Electronic Theses and Dissertations. 2081.
https://digitalcommons.georgiasouthern.edu/etd/2081
Research Data and Supplementary Material
No
