The construction and implementation direction of personalized learning model based on multimodal data fusion in the context of intelligent education

Abstract

The rapid development of artificial intelligence (AI) technologies, represented by computer vision, natural language processing, and speech recognition, has brought new opportunities for the advancement of personalized learning within intelligent education. This article utilizes intelligent collection devices such as cameras, electroencephalographs (EEG), eye trackers, smart bracelets, and data gloves to comprehensively collect and analyze data on learners’ voices, videos, texts, breathing, heartbeats, EEG signals, and eye movements. A multimodal dataset for learners is constructed across four dimensions: behavioral representation, physiological information, human–computer interaction, and learning context. By employing natural language processing, speech recognition, computer vision, and physiological information recognition technologies, we extract and analyze the multimodal datasets. This process mines the hidden personalized information of learners, enabling data-driven, real-time, quantified evaluation of their learning states. This study constructs a personalized learning model based on multimodal data fusion within the field of intelligent education by examining the current research landscape, data types, and relevant fusion strategies of this technology. It aims to provide personalized services tailored to the needs of each learner.