  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.
