Evaluation Method of Teaching Styles Based on Multi-modal Fusion

Wenyan Tang, Chongwen Wang, Yi Zhang
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Abstract

Teaching style refers to the teaching performance of the teacher's personal characteristics, teaching skills and teaching methods formed in the long-term teaching process. It plays an important role in helping students achieve academic success. With the continuous reform of the teaching system, more and more courses are taught in the form of videos. In this paper, we established a teaching style evaluation system and classification model based on teachers’ teaching behavior based on facial expressions, voices and postures in the teaching process. We adopted principal component analysis and autoencoder feature selection methods, and designed based on The multi-modal step-by-step fusion algorithm of deep neural network classifies teaching styles.
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基于多模态融合的教学风格评价方法
教学风格是指教师在长期教学过程中形成的个人特点、教学技巧和教学方法的教学表现。它在帮助学生取得学业成功方面起着重要作用。随着教学体制的不断改革,越来越多的课程以视频的形式进行教学。本文基于教师在教学过程中的面部表情、声音和姿态,建立了基于教师教学行为的教学风格评价体系和分类模型。采用主成分分析和自编码器特征选择方法,设计了基于深度神经网络的多模态分步融合教学风格分类算法。
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