Integration of Deep Learning for Student Learning Style Analysis in Adaptive Digital Learning

Abstract

This study explores the integration of deep learning techniques for analyzing students’ learning styles within adaptive digital learning systems. The rapid growth of digital education has created opportunities for personalized learning; however, traditional methods of identifying learning styles remain limited in accuracy and scalability. This research aims to develop a conceptual model that leverages deep learning to identify student learning preferences based on behavioral data. A qualitative approach using a systematic literature review was employed, analyzing relevant studies from reputable academic databases. The findings indicate that deep learning models—particularly Deep Neural Networks (DNN), Recurrent Neural Networks (RNN), and Long Short-Term Memory (LSTM)—can effectively process large-scale interaction data to detect dynamic learning patterns. The proposed model integrates data acquisition, deep learning analysis, and adaptive implementation to enable real-time personalization of learning content and pathways. The results highlight that deep learning enhances learning engagement, improves learning outcomes, and supports scalable personalization. Despite challenges such as data privacy and computational complexity, the integration of deep learning offers a promising solution for developing intelligent and responsive adaptive learning systems.