Understanding the Teaching Styles by an Attention based Multi-task Cross-media Dimensional Modeling

Suping Zhou, Xiang Li, Zeyang Ye, Jia J. Jia, Yang Yao, Kehua Lei, Jialie Shen, Yufeng Yin, Ying Zhang, Yan Huang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Teaching style plays an influential role in helping students to achieve academic success. In this paper, we explore a new problem of effectively understanding teachers' teaching styles. Specifically, we study 1) how to quantitatively characterize various teachers' teaching styles for various teachers and 2) how to model the subtle relationship between cross-media teaching related data (speech, facial expressions and body motions, content et al.) and teaching styles. Using the adjectives selected from more than 10,000 feedback questionnaires provided by an educational enterprise, a novel concept called Teaching Style Semantic Space (TSSS) is developed based on the pleasure-arousal dimensional theory to describe teaching styles quantitatively and comprehensively. Then a multi-task deep learning based model, Attention-based Multi-path Multi-task Deep Neural Network (AMMDNN), is proposed to accurately and robustly capture the internal correlations between cross-media features and TSSS. Based on the benchmark dataset, we further develop a comprehensive data set including 4,541 full-annotated cross-modality teaching classes. Our experimental results demonstrate that the proposed AMMDNN outperforms (+0.0842% in terms of the concordance correlation coefficient (CCC) on average) baseline methods. To further demonstrate the advantages of the proposed TSSS and our model, several interesting case studies are carried out, such as teaching styles comparison among different teachers and courses, and leveraging the proposed method for teaching quality analysis.

Original languageEnglish
Title of host publicationMM 2019 - Proceedings of the 27th ACM International Conference on Multimedia
PublisherAssociation for Computing Machinery, Inc
Pages1322-1330
Number of pages9
ISBN (Electronic)9781450368896
DOIs
Publication statusPublished - 15 Oct 2019
Event27th ACM International Conference on Multimedia, MM 2019 - Nice, France
Duration: 21 Oct 201925 Oct 2019

Publication series

NameMM 2019 - Proceedings of the 27th ACM International Conference on Multimedia

Conference

Conference27th ACM International Conference on Multimedia, MM 2019
CountryFrance
CityNice
Period21/10/201925/10/2019

Keywords

  • Attention
  • Multi-task
  • Teaching styles

ASJC Scopus subject areas

  • Media Technology
  • Computer Science(all)

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