  Education has a significant impact on both society and personal life. With
the development of technology, online education has been growing rapidly over
the past decade. While there are several online education studies on student
behavior analysis, the course concept mining, and course recommendations (Feng,
Tang, and Liu 2019; Pan et al. 2017), there is little research on evaluating
teachers' performance in online education. In this paper, we conduct a
systematic study to understand and effectively predict teachers' performance
using the subtitles of 1,085 online courses. Our model-free analysis shows that
teachers' verbal cues (e.g., question strategy, emotional appealing, and
hedging) and their course structure design are both significantly correlated
with teachers' performance evaluation. Based on these insights, we then propose
a hierarchical course BERT model to predict teachers' performance in online
education. Our proposed model can capture the hierarchical structure within
each course as well as the deep semantic features extracted from the course
content. Experiment results show that our proposed method achieves significant
gain over several state-of-the-art methods. Our study provides a significant
social impact in helping teachers improve their teaching style and enhance
their instructional material design for more effective online teaching in the
future.
