Electroencephalogram-based deep learning framework for the proposed solution of e-learning challenges and limitations. (12th April 2022)

• Record Type: Journal Article
• Authors: Pathak, Dharmendra; Kashyap, Ramgopal
• Is Part Of: International journal of intelligent information and database systems. Volume 15:Number 3(2022)
• Page Start: 295
• Page End: 310
• Publication Date: 2022-04-12
• Subjects: automated framework -- convolution neural network -- CNN -- deep learning -- EEG data -- e-learning -- electronic learning -- feature extraction -- machine learning; Database management -- Computer programs -- Periodicals; Information retrieval -- Computer programs -- Periodicals; Information storage and retrieval systems -- Computer programs -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Intelligent agents (Computer software) -- Periodicals; 006.33
• Languages: English
• ISSNs: 1751-5858

Abstract

There is a high surge in usage of online e-learning platforms due to the current ongoing COVID-19 scenario. There are specific problems that persist in the current e-learning online models, i.e., validations and tracking of students' learning curves, validation of presented course material, content-based personalisation as per the requirements of the students, identification of learning disabilities among students, etc. Our paper proposes the deep learning model to solve the issues related to existing machine learning models of manual feature extraction and training on limited data. Also, real-time e-learning data will be collected from students wearing EEG-headband while taking online classes. It solves the issues associated with conventional machine learning models and historical data. The proposed CNN model will classify the students on different grades and help in the development of an automated framework for the tracking of a student learning curve, providing recommendations for the betterment of e-learning course materials.