Prediction of Attentiveness of Students in a Traditional Teaching Environment
Abstract
This research seeks to increase understanding of how people interact and learn with technology with respect to cyberlearning. This research will focus on understanding how college students interact and learn by the utilization of physiological sensors in a traditional teaching environment to capture metrics related to attentiveness. Using blink rate, blink duration, and gaze position parameter data regarding students viewing a traditional PowerPoint presentation related to the Internet of things (IoT), an attentiveness metric was derived for each parameter then an overall parameter was summed. A prediction model using was then trained and tested using Adaptive Neuro Fuzzy Inference System (ANFIS) to predict attentiveness Using blink rate, blink duration, and gaze position parameter data.
Subject Area
Computer Engineering|Computer science|Electrical engineering
Recommended Citation
Samer Al Musawi,
"Prediction of Attentiveness of Students in a Traditional Teaching Environment"
(2023).
ETD Collection for Tennessee State University.
Paper AAI30314551.
https://digitalscholarship.tnstate.edu/dissertations/AAI30314551