TY - GEN
T1 - Understanding the Teaching Styles by an Attention based Multi-task Cross-media Dimensional Modeling
AU - Zhou, Suping
AU - Li, Xiang
AU - Ye, Zeyang
AU - Jia, Jia J.
AU - Yao, Yang
AU - Lei, Kehua
AU - Shen, Jialie
AU - Yin, Yufeng
AU - Zhang, Ying
AU - Huang, Yan
PY - 2019/10/15
Y1 - 2019/10/15
N2 - 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.
AB - 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.
KW - Attention
KW - Multi-task
KW - Teaching styles
UR - http://www.scopus.com/inward/record.url?scp=85074868457&partnerID=8YFLogxK
U2 - 10.1145/3343031.3351059
DO - 10.1145/3343031.3351059
M3 - Conference contribution
AN - SCOPUS:85074868457
T3 - MM 2019 - Proceedings of the 27th ACM International Conference on Multimedia
SP - 1322
EP - 1330
BT - MM 2019 - Proceedings of the 27th ACM International Conference on Multimedia
PB - Association for Computing Machinery, Inc
T2 - 27th ACM International Conference on Multimedia, MM 2019
Y2 - 21 October 2019 through 25 October 2019
ER -