A Survey of EEG-Based Approaches to Classroom Attention Assessment in Education

Abstract

In evaluating classroom teaching quality, students’ attention assessment is a critical indicator in education management, as it holds significant practical value for improving teaching methods and instructional quality. Electroencephalogram (EEG) signals can monitor dynamic neural activity in the brain in real time. Their objectivity and non-invasive nature make them particularly suitable for attention assessment in classroom environments. This article first provides a brief overview of existing attention assessment methods, and then presents a comprehensive review of the current research status and methodologies in EEG-based attention assessment, including signal acquisition, preprocessing, feature extraction and selection, classification, and evaluation. Subsequently, the challenges in EEG-based teaching attention assessment are discussed, including the acquisition of high-quality signals, multimodal data fusion, complexity of data, and hardware setups for deep learning method implementation. Finally, a multimodal classroom attention assessment method, which integrates EEG and eye movement signals, is proposed to enhance teaching management.