Predict Students’ Attention in Online Learning Using EEG Data

In education, it is critical to monitor students’ attention and measure the extents to which students participate and the differences in their levels and abilities. The overall goal of this study was to increase the quality of distance education. In particular, in order to craft an approach that wil...

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Bibliographic Details
Published in:Sustainability 2022-06, Vol.14 (11), p.6553
Main Authors: Al-Nafjan, Abeer, Aldayel, Mashael
Format: Article
Language:English
Subjects:
Algorithms
COVID-19
Datasets
Detectors
Distance learning
Education
EEG
Electroencephalography
Equipment and supplies
Fourier transforms
Human-computer interface
Online education
Online instruction
Students
Support vector machines
Sustainability
Teachers
Teaching
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Summary:In education, it is critical to monitor students’ attention and measure the extents to which students participate and the differences in their levels and abilities. The overall goal of this study was to increase the quality of distance education. In particular, in order to craft an approach that will effectively augment online learning using objective measures of brain activity, we propose a brain–computer interface (BCI) system that aims to use electroencephalography (EEG) signals for the detection of student’s attention during online classes. This system will aid teachers to objectively assess student attention and engagement. To this end, experiments were conducted on a public dataset; we extracted power spectral density (PSD) features using used a fast Fourier transform. Different attention indexes were calculated. Then, we built three different classification algorithms: k-nearest neighbors (KNN), support vector machine (SVM), and random forest (RF). Our proposed random forest classifier achieved a higher accuracy (96%) than KNN and SVM. Moreover, our results compared to state-of-the-art attention-detection systems with respect to the same dataset. Our findings revealed that the proposed RF approach can be used to effectively distinguish the attention state of a user.
ISSN:2071-1050
2071-1050
DOI:10.3390/su14116553