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AI Technology for Enhanced Learning Ability of Learners Using EEG Signals
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Author(s): Rabi Shaw (Dr. B.R. Ambedkar National Institute of Technology, Jalandhar, India), Simanjeet Kalia (Kalinga Institute of Industrial Technology, India), Sourabh Mohanty (Kalinga Institute of Industrial Technology, India)and Tapas Behera (Kalinga Institute of Industrial Technology, India)
Copyright: 2025
Pages: 34
Source title:
Humanizing Technology With Emotional Intelligence
Source Author(s)/Editor(s): Subrata Tikadar (Amity University, Kolkata, India), Haipeng Liu (Coventry University, UK), Pronaya Bhattacharya (Amity University Kolkata, India)and Samit Bhattacharya (Indian Institute of Technology Guwahati, India)
DOI: 10.4018/979-8-3693-7011-7.ch006
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Abstract
This review paper explores recent educational research efforts to enhance pedagogical methodologies, particularly within Flipped Learning (FL). The focus is on innovative strategies aimed at optimizing the FL model, with an emphasis on sustaining student attention during pre-loaded lecture videos, a critical component of FL. Researchers have used EEG signals to analyze cognitive states and detect non-attentive behavior. A notable study introduces the Lecture Video Recommendation in Flipped Learning (LRFL) system, which uses unsupervised learning to categorize student behaviors and recommend revisiting lessons. Additionally, advancements in feature extraction methods like 1D Multi-Point Local Ternary Pattern (1D MP-LTP) are explored to improve classification accuracy. Another study proposes a specialized deep reinforcement learning algorithm, Double Deep Q-Network (DDQN), to quantify attention levels from EEG signals. These findings highlight the potential of emerging technologies and data-driven approaches to enhance student engagement and learning outcomes in digital education.
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