Evaluation Method of Teaching Styles Based on Multi-modal Fusion

Wen Tang, Chongwen Wang, Yi Zhang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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.

Original languageEnglish
Title of host publication2021 7th International Conference on Communication and Information Processing, ICCIP 2021
PublisherAssociation for Computing Machinery
Pages9-15
Number of pages7
ISBN (Electronic)9781450385190
DOIs
Publication statusPublished - 16 Dec 2021
Event7th International Conference on Communication and Information Processing, ICCIP 2021 - Virtual, Online, China
Duration: 16 Dec 202118 Dec 2021

Publication series

NameACM International Conference Proceeding Series

Conference

Conference7th International Conference on Communication and Information Processing, ICCIP 2021
Country/TerritoryChina
CityVirtual, Online
Period16/12/2118/12/21

Keywords

  • Deep neural network
  • Feature selection
  • Multi-modal fusion
  • Teaching styles

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