首页|基于多尺度特征图卷积网络的教学行为识别及分析

基于多尺度特征图卷积网络的教学行为识别及分析

扫码查看
在教育领域,课堂教学评价是提高教学质量的关键环节之一.随着数字化教育的推广,寻求一种智能化的评价方法变得尤为重要.为此,提出了一种基于骨架行为识别和滞后序列分析的新型方法,旨在更准确地对教师的教学行为进行捕获和分析,在减少人力资源消耗的同时,降低教学评价的主观性.首先,提出多尺度特征图卷积网络,并将其用于教师课堂行为分析.该网络在空间维度上使用多尺度语义特征融合模块捕捉骨架点和肢体部位两个尺度的特征;在时间维度上使用多尺度时序特征提取模块,并分别从全局和局部两个角度提取骨架数据的时间特征.然后,构建了教师课堂行为分析数据集,并在该数据集上验证了所提方法的有效性.最后,利用所提的骨架行为识别模型和滞后序列分析法,搭建了一套教学行为识别与分析系统.在进行不同课堂教学行为识别时,所提方法在教室行为识别与分析方面具有显著的优势.
Recognition and Analysis of Teaching Behavior Based on Multi-scale GCN
In the field of education,classroom teaching evaluation stands as a pivotal element in enhancing teaching quality.With the widespread adoption of digital education,the quest for an intelligent evaluation method becomes increasingly crucial.There-fore,this paper proposes a novel method based on skeleton action recognition and lagged sequence analysis,aiming to more accu-rately capture and analyze teachers'teaching behaviors while reducing manpower consumption and diminishing the subjectivity of teaching evaluations.Firstly,a multi-scale feature graph convolutional network is proposed and applied to analyze teacher class-room behaviors.This network utilizes a multi-scale semantic feature fusion module to capture features at two scales,skeleton points,and body parts,in the spatial dimension.In the temporal dimension,a multi-scale temporal feature extraction module is employed to extract temporal features of skeleton data from both global and local perspectives.Subsequently,a dataset for analy-zing teachers'classroom behaviors is constructed,and the effectiveness of the proposed method is validated on this dataset.Final-ly,leveraging the proposed skeleton action recognition model and lagged sequence analysis,a system for recognizing and analyzing teaching behaviors is developed.The proposed method demonstrates significant advantages in classroom behavior recognition and analysis when applied to various classroom teaching scenarios.

Teaching behavior analysisSkeleton sequenceDigital educationGraph convolutionAction recognition

李佳楠、李锐宜、赵至夫、宋娟、韩嘉泷、朱桐

展开 >

西安电子科技大学计算机科学与技术学院 西安 710071

西安电子科技大学人工智能学院 西安 710071

西安电子科技大学前沿交叉研究院 西安 710071

教师行为分析 骨架序列 数字化教育 图卷积网络 行为识别

西安电子科技大学教育教学改革重点项目中央高校基本科研业务费项目中央高校基本科研业务费项目国家自然科学基金青年科学基金国家自然科学基金青年科学基金

A2304ZYTS24092QTZX240856220235662302373

2024

计算机科学
重庆西南信息有限公司(原科技部西南信息中心)

计算机科学

CSTPCD北大核心
影响因子:0.944
ISSN:1002-137X
年,卷(期):2024.51(10)