• Journal of Internet of Things and Convergence
• /
• v.10 no.5
• /
• pp.173-178
• /
• 2024

This paper proposes the design of a personalized learning support system for students with learning disabilities, utilizing biometric signals. The system leverages EEG (electroencephalography) and eye-tracking data to monitor the learner's state in real-time, identifying signs of decreased concentration, boredom, or diminished interest. By providing customized feedback and an adaptive learning environment, the system aims to enhance the learning experience and effectiveness. Key components of the system include data collection using Emotiv Epoc X and eye-tracking devices, data preprocessing, and the application of AI models such as Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks. Additionally, Random Forest and Gradient Boosting techniques are employed to predict learner characteristics and optimize feedback, while Decision Trees are used to analyze learning outcomes and deliver individualized recommendations. The proposed system aims to provide an optimal learning environment for students with learning disabilities, with the ultimate goal of improving educational performance and motivation.