TY - GEN
T1 - Learner Attention Quantification Using Eye Tracking and EEG Signals
AU - Hossain, Md Shakil
AU - Pandya, Dhruv
AU - Allen, Andrew
AU - Hamza-Lup, Felix G.
N1 - Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2023
Y1 - 2023
N2 - We adapted an application called Non-Intrusive Classroom Attention Tracking System (NiCATS) that quantifies and generates statistical data based on a student’s attention level while performing various tasks like coding, browsing through websites, or reading lecture notes on computers. This research is focused on understanding how student attentiveness can be measured using eye-tracking (e.g. gaze points) and Electroencephalogram (EEG) signals data. By leveraging the existing NiCATS with new integration of BCI devices we explore the possibilities of identifying correlations of EEG signals with students’ attention during classroom. Two Eye metrics (number of saccades and total fixations) have slight positive correlation with all the EEG bands (theta, alpha, etc.) except the delta band. The result of this analysis is an additional step toward providing instructors feedback on the effectiveness of instructional design as measured by attentiveness of students in their classroom.
AB - We adapted an application called Non-Intrusive Classroom Attention Tracking System (NiCATS) that quantifies and generates statistical data based on a student’s attention level while performing various tasks like coding, browsing through websites, or reading lecture notes on computers. This research is focused on understanding how student attentiveness can be measured using eye-tracking (e.g. gaze points) and Electroencephalogram (EEG) signals data. By leveraging the existing NiCATS with new integration of BCI devices we explore the possibilities of identifying correlations of EEG signals with students’ attention during classroom. Two Eye metrics (number of saccades and total fixations) have slight positive correlation with all the EEG bands (theta, alpha, etc.) except the delta band. The result of this analysis is an additional step toward providing instructors feedback on the effectiveness of instructional design as measured by attentiveness of students in their classroom.
KW - Brain computer interface
KW - Classroom attention tracking
KW - Electroencephalogram (EEG)
KW - Eye-tracking
UR - https://www.scopus.com/pages/publications/85142114571
U2 - 10.1007/978-3-031-18458-1_57
DO - 10.1007/978-3-031-18458-1_57
M3 - Conference article
AN - SCOPUS:85142114571
SN - 9783031184574
T3 - Lecture Notes in Networks and Systems
SP - 836
EP - 847
BT - Proceedings of the Future Technologies Conference, FTC 2022, Volume 2
A2 - Arai, Kohei
PB - Springer Science and Business Media Deutschland GmbH
T2 - 7th Future Technologies Conference, FTC 2022
Y2 - 20 October 2022 through 21 October 2022
ER -