Deep Learning-Based Modeling Methods in Personalized Education

Abstract

Deep learning has significantly transformed personalized education by enabling intelligent adaptation to individual learning needs. This study explores deep learning-based modeling methods that enhance personalized learning experiences, optimize instructional content, and predict student progress. We examine key techniques, including recurrent neural networks (RNNs), transformers, reinforcement learning, and multimodal learning analytics, to demonstrate their roles in personalized learning path recommendations and adaptive content generation. Case studies of AI-driven tutoring systems and learning management platforms illustrate real-world applications. Additionally, we address challenges related to data privacy, algorithmic bias, and model interpretability. The paper concludes with future directions for deep learning in education, emphasizing its potential for enhancing immersive and intelligent learning environments.