IoT 환경 기반의 뇌파 및 시선 추적을 활용한 학습 패턴 분석 시스템 설계
Design of a learning pattern analysis system using brain waves and eye tracking based on IoT environment
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초록
본 논문은 학습 장애를 가진 학생들을 위한 생체신호 기반 맞춤형 학습 지원 시스템의 설계를 제안한다. 이시스템은 EEG(뇌파)와 시선 추적 데이터를 활용하여 학습자의 상태를 실시간으로 모니터링하고, 집중력 저하, 지루함또는 흥미 감소를 식별한다. 이를 통해 맞춤형 피드백과 적응형 학습 환경을 제공함으로써 학습 경험과 효과를 향상시키고자 한다. 시스템의 주요 구성 요소로는 Emotiv Epoc X와 시선 추적 장치를 사용한 데이터 수집, 수집된 데이터의전처리, 그리고 CNN(Convolutional Neural Networks) 및 LSTM(Long Short-Term Memory)과 같은 AI 모델의적용이 포함된다. 또한 Random Forest와 Gradient Boosting을 활용하여 학습자 특성을 예측하고 피드백을 최적화하며, Decision Trees를 통해 학습 성과를 분석하고 개별 맞춤형 피드백을 제공한다. 제안된 시스템은 학습 장애 학생들에게 최적의 학습 환경을 제공하여 교육적 성과와 동기를 향상시키는 것을 목표로 한다.
Abstract
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.
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