Frontiers in Educational Research, 2024, 7(12); doi: 10.25236/FER.2024.071220.
Chuyu Zhang
School of Education and Physical Education, Yangtze University, Jingzhou City, China
This paper explores optimizing and implementing an education-theory-driven intelligent learning system based on machine learning. The system integrates Convolutional Neural Networks and Multi-Layer Perceptrons to provide accurate, personalized learning recommendations. Performance evaluation shows a recommendation accuracy of over 92% and a response time of under one second. A comparative analysis with existing systems highlights the system’s superior precision and user-friendly interface. However, limitations such as data privacy, model transparency, and scalability across devices are identified. Future improvements include adopting explainable artificial intelligence techniques, federated learning for privacy, and enhanced system architecture for cross-platform compatibility. This research aims to advance intelligent learning systems by integrating educational theory with machine learning to create personalized, practical learning experiences.
adaptive education, machine learning, personalized recommendations, AI-driven pedagogy, data privacy
Chuyu Zhang. Research on Optimization and Implementation of Education-Theory-Driven Intelligent Learning System Based on Machine Learning. Frontiers in Educational Research (2024) Vol. 7, Issue 12: 133-138. https://doi.org/10.25236/FER.2024.071220.
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