In-class student learning status assessment system based on wearable physiological signal monitoring

Abstract

The invention discloses an in-class student learning status assessment system based on wearable physiological signal monitoring. The in-class student learning status assessment system based on the wearable physiological signal monitoring comprises a wearable multi-physiological signal acquisition module, a physiological signal transmission and synchronization module, a server-side data analysis module, and a learning status assessment module. The wearable multi-physiological signal acquisition module acquires electroencephalogram signals, electrocardiogram signals and pulse wave signals of students; the physiological signal transmission and synchronization module synchronizes time stamps of the various physiological signals, and transmits data to the server-side data analysis module in a wireless way; the server-side data analysis module performs pretreatments on the various physiological signals, carries out attribute extraction, inputs attribute vectors into a classification model soas to obtain learning state indexes of students, and uploads the learning state indexes to the learning status assessment module; and the learning status assessment module displays data uploaded by the server-side data analysis module to a teacher. Compared with existing evaluation ways, the in-class student learning status assessment system based on the wearable physiological signal monitoring is more intelligent, objective and accurate; and thus, the system is conducive to promotion of teaching level and teaching quality.

Description

technical field [0001] The invention belongs to the field of educational informatization, and in particular relates to a system for evaluating students' classroom learning status. Background technique [0002] The effective evaluation of students' classroom learning status is an important means to promote students' progress and improve the quality of classroom teaching. The rapid development of signal processing, big data, artificial intelligence and other technologies will become endogenous variables that support and lead the modernization of education and promote the upgrading of education informatization. [0003] With the development of wearable device technology and signal processing algorithms, physiological signals are widely used to monitor mental states such as emotion, attention, stress, and fatigue. The multi-physiological signals collected based on wearable devices can comprehensively and accurately reflect the state of the measured object and improve the overal...

AI Technical Summary

Problems solved by technology

Teachers make adjustments and improvements after observing abnormalities. Teachers often cannot take into account the learning status of all students, and the evaluation methods are highly subjective.

Although the method of video surveillance is simple, it has great defects, including low robustness, large external influence factors, inability to recognize false facial expressions, and high requirements for real-time computing. More importantly, the collection of facial videos involves violations of student privacy. problem, use on campus has been strictly limited and managed

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