Cognitive State Analysis: Enhancing Educational Outcomes through EEG-Based Confusion Prediction

Modern educational institutions typically employ three major learning styles, which are conventional, e-learning, and flipped classroom. Traditional teaching is face to face where students only record notes rather than understand the meaning behind the information that they learn. However online learning is characterized by a relationship gap between student and faculty which can hinder personalized feedbacks or interactions Flipped teaching enables an initial understanding through pre- class preparation of materials. However it does not allow teachers to determine how focused students are in class as well as their comprehension levels. Understanding learners’ cognitive states such as misunderstanding is essential in improving academic outcomes and guiding teaching tactics. Electroencephalography (EEG), a technique that records electrical activity in the brain offers a new approach to understanding engagement in learning among students. The use of EEG during the lessons may help to detect if students really understand what they have learned thus this can solve some drawbacks of the current ways of education. The proposed methodological framework seeks to build up a robust model that predicts whether a learner is confused or not based on his/her EEG brainwave patterns. It will involve gathering EEG data from pupils during their learning sessions which would then be used to detect particular brain wave patterns that are linked with confusion. As a result, machine learning algorithms will be applied to teach and test, in order to ensure the prediction of confusion among students is done accurately. This will enable instructors receive immediate feedback on student understanding through real-time integration of such predictive models into educational contexts; thus it enables them to intervene or support as soon as possible. In this way, not only does it improve the learning experience but also creates a bridge between how teachers offer instructions and students comprehend them thus creating an efficient education environment.

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Published
2025-03-06
DOI
https://doi.org/10.1109/rait65068.2025.11088938
Primary Topic
EEG and Brain-Computer Interfaces
Type
article
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article

Cognitive State Analysis: Enhancing Educational Outcomes through EEG-Based Confusion Prediction

Subhranil Som, Rabi Shaw, S. Shaw, Simanjeet Kalia
1 links
EEG and Brain-Computer Interfaces
article

Cognitive State Analysis: Enhancing Educational Outcomes through EEG-Based Confusion Prediction

Subhranil Som, Rabi Shaw, S. Shaw, Simanjeet Kalia
article en

Abstract

Modern educational institutions typically employ three major learning styles, which are conventional, e-learning, and flipped classroom. Traditional teaching is face to face where students only record notes rather than understand the meaning behind the information that they learn. However online learning is characterized by a relationship gap between student and faculty which can hinder personalized feedbacks or interactions Flipped teaching enables an initial understanding through pre- class preparation of materials. However it does not allow teachers to determine how focused students are in class as well as their comprehension levels. Understanding learners’ cognitive states such as misunderstanding is essential in improving academic outcomes and guiding teaching tactics. Electroencephalography (EEG), a technique that records electrical activity in the brain offers a new approach to understanding engagement in learning among students. The use of EEG during the lessons may help to detect if students really understand what they have learned thus this can solve some drawbacks of the current ways of education. The proposed methodological framework seeks to build up a robust model that predicts whether a learner is confused or not based on his/her EEG brainwave patterns. It will involve gathering EEG data from pupils during their learning sessions which would then be used to detect particular brain wave patterns that are linked with confusion. As a result, machine learning algorithms will be applied to teach and test, in order to ensure the prediction of confusion among students is done accurately. This will enable instructors receive immediate feedback on student understanding through real-time integration of such predictive models into educational contexts; thus it enables them to intervene or support as soon as possible. In this way, not only does it improve the learning experience but also creates a bridge between how teachers offer instructions and students comprehend them thus creating an efficient education environment.

National Allergy Asthma Bronchitis Institute (IN), West Bengal State University (IN), KIIT University (IN), Dr. B. R. Ambedkar National Institute of Technology Jalandhar (IN)
Openalex Percentile: Top 82%
EEG and Brain-Computer Interfaces
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