A Review of Research on Intelligent Assessment of Student Attention in the Classroom Based on Multimodal Data Fusion

Yukai Fu

doi:10.47297/wspeiWSP2516-253511.20261002

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A Review of Research on Intelligent Assessment of Student Attention in the Classroom Based on Multimodal Data Fusion

10卷第2期

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A Review of Research on Intelligent Assessment of Student Attention in the Classroom Based on Multimodal Data Fusion
A Review of Research on Intelligent Assessment of Student Attention in the Classroom Based on Multimodal Data Fusion
School of Mathematics and Information Science, South China Agricultural University, Guangzhou, Guangdong, 510630, China
2026年10卷第2期页码：42-47
纸质出版：2026-04-30
DOI：10.47297/wspeiWSP2516-253511.20261002
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Yukai Fu. (2026). A Review of Research on Intelligent Assessment of Student Attention in the Classroom Based on Multimodal Data Fusion. 创业与创新（英文版）, 10(2), 42-47.

Fu Yukai.A Review of Research on Intelligent Assessment of Student Attention in the Classroom Based on Multimodal Data Fusion[J].Entrepreneurship and Innovation,2026,10(02):42-47.
Yukai Fu. (2026). A Review of Research on Intelligent Assessment of Student Attention in the Classroom Based on Multimodal Data Fusion. 创业与创新（英文版）, 10(2), 42-47. DOI： 10.47297/wspeiWSP2516-253511.20261002.

Fu Yukai.A Review of Research on Intelligent Assessment of Student Attention in the Classroom Based on Multimodal Data Fusion[J].Entrepreneurship and Innovation,2026,10(02):42-47. DOI： 10.47297/wspeiWSP2516-253511.20261002.

摘要

Abstract

Against the backdrop of the deep integration of Education Informatization 2.0 and blended learning

the precise assessment of student attention in the classroom has become a core requirement for optimizing teaching strategies and enhancing teaching quality. Traditional manual observation methods suffer from limitations such as high subjectivity

delayed feedback

and limited coverage

making them ill-suited for dynamic

large-scale classroom settings. Multimodal data fusion technology

by integrating multi-dimensional data such as visual

audio

and behavioral data

provides a data-driven approach to classroom attention assessment.This paper systematically reviews research progress in the field of intelligent classroom student attention assessment based on multimodal data fusion

focusing on methods for collecting and preprocessing multi-source educational data

the evolution and technical classification of intelligent attention assessment algorithms

and the current status of model applications in real classroom environments;It conducts an in-depth analysis of core challenges facing current research

including the trade-off between scenario adaptability and low intrusiveness

the balancing act between accuracy

real-time performance

and interpretability

and the insufficient correlation between assessment metrics and learning outcomes; finally

it outlines future research directions

aiming to provide theoretical references and literature support for the development of intelligent classroom attention assessment systems characterized by low intrusiveness

high reliability

and strong interpretability.

关键词

Keywords

references

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